Acid Deposition and Acidification of Soil and Water in the Tie Shan Ping Area, Chongqing, China

Chongqing is among the heaviest polluted cities in China. Combustion of coal with relatively high sulfur content causes high sulfur emission and deposition in the area. Effects on soils and waters of the acid deposition in the Chongqing area have been studied in the field at a forested site outside the city. Deposition chemistry and fluxes, soil and soil water chemistry as well as surface water chemistry are presented for the period 1996–1998. There are some stress symptoms at the forest in the area and severe forest damage has been reported at Nanshan, closer to Chongqing center. Monitoring of the acidification situation in the area must be followed closely as impacts may be expected if the deposition is not reduced in the future. The deposition of sulfur, H⁺ as well as calcium at the site is high. Wet deposition of sulfur is estimated to 4.7 – 5.7 g S m^?2 yr^?1 during the three years sampled; dry deposition is probably of similar size. Annual volume-weighted pH in bulk deposition was 4.0 – 4.2 and the calcium wet deposition flux was 2.6 – 3.6 g Ca²⁺ m^?2. There are considerable seasonal variations in the concentrations, related to the seasonal variations in precipitation amount (dry winter, wet summer). The soils at the site are acid with median base saturation of 12% and 8% in the topsoil and subsoil, respectively. In soil water, aluminum concentrations are typically in the range 3–8 mg L^?1. However, due to the high base cation deposition, the Al/(Ca²⁺+Mg²⁺) molar ratio is below unity in most samples, indicating little damage of forest due to aluminum in soil water.     

The effect on model identifiability of allowing different relocation rates for live and dead animals in the combined analysis of telemetry and recapture data

Models are described for the joint analysis of live-trapping and radio telemetry data from a study on black bears (Ursus americanus) in which all animals received ear tags and a subset also received radio tags. Concerns about bias in survival estimates led to investigation of identifiability and estimator precision for a series of models that allowed differenttelemetry relocation rates for living and dead animals, in addition to emigration and seasonal variation in survival. Identifiability was determined by showing that the expected information matrix was nonsingular. Models with fidelity constant across time, and with the same degree of time specificity for survival rates and relocation rates for dead animals, were determined to be nonidentifiable. More general models, with a greater degree of time specificity for survival rates, were near-singular, and estimators under these near-singular models had poor precision. Analysis of data from the study on black bears illustrated that estimates of survival have poor precision when relocation rates are estimated separately for live and dead animals. It is recommended that the effort expended to relocate both living and dead animals be consistently high in each telemetry survey, so that relocation rates will be high and constant across time and mortality status.     

A system for evaluation of growth and mortality in Russian forests

A modelling system (MS) for evaluation of current increment (net and gross growth) and mortality in Russian forests is presented. The informational basis of the MS are the data of the Russian forest state account (growing stock by dominant species, age, site indices, stocking) and some additional indicators (types of age structure, average species composition, type and intensity of disturbance regimes by ecoregions and/or landscapes. The MS includes model blocks generated with available information including yield tables (general, regional, full-stocked, with different stocking and types of age structure), growth tables, models, and experimental data for managed and unmanaged, disturbed and undisturbed forests. Two-dimensional (by age and site index) and three-dimensional (age, site index and stocking) models by species and ecoregions, based on regularities of coefficients of Mitscherlich growing functions, were used as the nuclei of the MS. The preliminary results, received for several Russian forest forming species gave a good fit to experimental data.     

Precipitation chemistry and atmospheric processes in the forested part of Croatia

The influence of mesoscale weather patterns on the chemical composition of daily precipitation samples is analysed. The data of pH, sulphur from sulphates and total nitrogen are analysed for two rural sites: Plitvice station in forested part of Central Croatia (1981 to 1990) and Puntijarka suburban station on the mountain near Zagreb, the capital of Croatia (1982–1991). The two prevailing weather types in precipitation days are selected and the comparison of chemical composition of precipitation is made for each of them. The frequency distributions of pH, sulphur and nitrogen show that concentration of major ions in precipitation apparently depends on the regional scale weather type. It is shown that the seasonal variation of deposition is related to the seasonal variation in precipitation amount. In both weather types Plitvice receives more pollution than Puntijarka that is closer to urban and industrial pollution sources. Both locations are under the prevailing influence of regional pollution sources.     

Characterizing Groundwater Dynamics Based on Impact of Pulp and Paper Mill Effluent Irrigation and Climate Variability

Automated synoptic weather typing and robust orthogonal stepwise regression analysis (via principal components analysis) were applied together to develop within-weather-type air pollution prediction models for a variety of pollutants (specifically, carbon monoxide – CO, nitrogen dioxide – NO₂, ozone – O₃, sulphur dioxide – SO₂, and suspended particles – SP) for the period 1974–2000 in south-central Canada. The SAS robust regression procedure was used to limit the influence of outliers on air pollution prediction algorithms. Six-hourly Environment Canada surface observed meteorological data and 6-hourly US National Centers for Environmental Prediction (NCEP) reanalysis data of various weather elements were used in the analysis. The models were developed using two-thirds of the total years for meteorological and air pollution data; the remaining one-third (randomly selected) was used for model validation. Robust stepwise regression analysis was performed to analytically determine the meteorological variables that might be used to predict air pollution concentrations. There was a significant correlation between observed daily mean air pollution concentrations and model predictions. About 20, 50, and 80% of the 80 prediction models across the study area possessed R ² values ≥ 0.7, 0.6, and 0.5, respectively. The results of model validation were similar to those of model development, with slightly smaller model R ² values.     

安太堡露天煤矿复垦区不同人工林土壤呼吸特征

[目的]探讨复垦模式对土壤呼吸作用的影响,同时为矿区复垦土地质量评价、复垦模式的筛选提供数据支撑。[方法]采用动态密闭气室红外CO2分析法对露天煤矿复垦区5块永久性样地土壤呼吸作用及其相关组分的日变化及季节动态进行跟踪测定。[结果]各样地土壤呼吸作用均呈现出明显的季节变化规律,但日变化趋势却各不相同。土壤呼吸速率日变化在5,9,10月份变幅较为平缓,6,7,8月份变幅较大,且在7,8月份达到最大值。去根系后,土壤温度及水分与未处理之间没有显著差异,但土壤呼吸速率值明显下降,下降幅度为19%~46%。土壤总呼吸速率和去根系土壤呼吸速率均与土壤温度、土壤水分、双因子呈幂或指数函数关系。[结论]刺槐纯林模式更有利于土壤的熟化与肥力的提高。     

曹家路流域面源污染的特征及规律

通过连续的野外试验,对北京市重要水源地密云水库水源区曹家路流域的土壤理化特征、面源污染产汇污机制、污染物迁移转化特征等进行了初步分析和研究。研究内容包括:不同土壤类型的理化特征及养分含量随深度的变化规律;一场短时暴雨水文情形下,各面源污染特征指标的变化规律及产汇污机制;不同土地覆盖条件下,流域内各子河段面源污染的变化特征。最后,针对该研究区域面源污染特征,提出了污染物削减及控制的相关对策。     

Land-use history,forest conversion,and soil organic carbon in pine plantations and native forests of south eastern Australia

Land-use history – the number, type, and duration of previous land uses – is relevant to many questions regarding land-use effects on soil carbon, but is infrequently reported. We examine the importance of land-use history variables, as well as topographic and edaphic variables, on soil C in a range of forest types – native forest, pine plantations, secondary forest and rehabilitated forest – at three contrasting locations in south eastern Australia. Our comparisons include a novel forest conversion of exotic pine plantations to native, broadleaf forest.Using nested ANOVAs, we detected few differences in soil C concentration indices (total C, microbial biomass C, K₂SO₄–C) and C content among eucalypt-dominated vegetation and pine plantations within each location (0–10 cm depth). However, planned contrasts indicated a 30% decrease in soil C content with conversion of native forest to pine plantation of age 37 years. The reverse land-use change – pine plantation to native, broadleaf forest – was associated with a decrease in soil C concentration and content at one location (40%; age 12–13 years) and no detectable changes at another (to age 7 years). Variable effect between locations of this novel land-use change on soil C could be due to differences in potential productivity, conifer species, and plantation age.We used correlation coefficients and general linear models to identify widely applicable variables for predicting soil C concentration and content at local scales (≤ 20 km²). Within-location relationships with topographic variables were weak and infrequent relative to those with edaphic and land-use history variables. Soil texture was strongly correlated with soil C at each location, although the relative significance of different particle size fractions differed among locations. Electrical conductivity appeared more widely applicable since it was included in C models at two locations. Combining land-use history and edaphic variables produced strong predictive models for soil C concentrations and content at two locations (total r² 0.83 to 0.95). Positive relationships were indicated between soil C and ‘age of current vegetation’ at one location, and negative relationships were indicated with ‘number of land uses’ at another. These data highlight a potential predictive role for land-use history variables in local-scale assessments of soil C in forested landscapes.     

Critical loads — Possibilities and constraints

Since critical thresholds for acidic deposition were first considered in the 1970s, the critical loads approach has rapidly evolved into a practical tool for addressing the problems of pollution control. It has gained acceptance from many scientists and policy makers, bridging the gap between science and policy and proving its use with the acidifying effects of the atmospheric pollutants of sulphur and nitrogen. For this there are examples at local, national and international scales and there appears to be scope for further applications as methods and databases improve. The UN/ECE used the approach to underpin the second Sulphur Protocol; for this, emission reductions were recommended on the basis of environmental effects as well as the costs of control measures. Although limited by a relatively coarse transport modelling scale, the advantages over non-effect-orientated approaches were evident. At a more local scale, the approach has often lacked information linking some of the chemical effects on soils and freshwaters to harmful effects to biota. In addition, the important consideration of rates of change, in particular recovery, of ecosystems with changing pollutant loads has still to be addressed nationally and internationally. The critical loads approach is a first step towards effects orientated pollution control. Already sophisticated methods, based upon similar dose-response data, are being developed; these lend themselves better to economic evaluation of damage. Despite limitations, the critical loads approach has proved a practical method for deriving pollution control strategies whose success will be judged in the years ahead.     

森林养分循环模型应用

The nutrient cycling model NuCM is one of the most detailed models for simulating processes that influence nutrient cycling in forest ecosystems. A field study was conducted at Tieshanping, a Masson pine （Pinus massoniana Lamb.） forest site, in Chongqing, China, to monitor the impacts of acidic precipitation on nutrient cycling. NuCM simulations were compared with observed data from the study site. The model produced an approximate fit with the observed data. It simulated the mean annual soil solution concentrations in the two simulation years, whereas it sometimes failed to reproduce seasonal variation. Even though some of the parameters required by model running were measured in the field, some others were still highly uncertain and the uncertainties were analyzed. Some of the uncertain parameters necessary for model running should be measured and calibrated to produce a better fit between modeled results and field data.     

Modelling in situ activities of enzymes as a tool to explain seasonal variation of soil respiration from agro-ecosystems

Understanding in situ enzyme activities could help clarify the fate of soil organic carbon (SOC), one of the largest uncertainties in predicting future climate. Here, we explored the role of soil temperature and moisture on SOM decomposition by using, for the first time, modelled in situ enzyme activities as a proxy to explain seasonal variation in soil respiration. We measured temperature sensitivities (Q₁₀) of three enzymes (β-glucosidase, xylanase and phenoloxidase) and moisture sensitivity of β-glucosidase from agricultural soils in southwest Germany. Significant seasonal variation was found in potential activities of β-glucosidase, xylanase and phenoloxidase and in Q₁₀ for β-glucosidase and phenoloxidase activities but not for xylanase. We measured moisture sensitivity of β-glucosidase activity at four moisture levels (12%–32%), and fitted a saturation function reflecting increasing substrate limitation due to limited substrate diffusion at low water contents. The moisture response function of β-glucosidase activity remained stable throughout the year. Sensitivity of enzymes to temperature and moisture remains one of the greatest uncertainties in C models. We therefore used the response functions to model temperature-based and temperature and moisture-based in situ enzyme activities to characterize seasonal variation in SOC decomposition. We found temperature to be the main factor controlling in situ enzyme activities. To prove the relevance of our modelling approach, we compared the modelled in situ enzyme activities with soil respiration data measured weekly. Temperature-based in situ enzyme activities explained seasonal variability in soil respiration well, with model efficiencies between 0.35 and 0.78. Fitting an exponential response function to in situ soil temperature explained soil respiration to a lesser extent than our enzyme-based approach. Adding soil moisture as a co-factor improved model efficiencies only partly. Our results demonstrate the potential of this new approach to explain seasonal variation of enzyme related processes.     

Assessing heavy metal contamination in soils of the Zagreb region (Northwest Croatia) using multivariate geostatistics

The assessment of soil contamination and location of pollution sources represent a crucial issue in soil remediation. Topsoil samples were collected in the Zagreb area (Northwest Croatia) and the total contents of trace and major elements were determined. A multivariate geostatistical analysis was used to estimate soil chemical composition variability. Factorial Kriging Analysis (FKA) was used to investigate the scale-dependent correlation structure of some variables by modelling co-regionalization of ten chemical variables, co-kriging specific factors and mapping them. The FKA provided two regionalized factors at different spatial scales of variability: the first factor at shorter range for Zn, Pb, Cd, Cu and Ni indicated different sources of anthropogenic contamination, whereas Ca (mainly loading on the longer range factor) was related to the lithology and parent material composition. The methodology used has proved to be a useful tool to separate geological and anthropogenic causes of variation in soil heavy metal content and to identify common pollution sources.     

Spent Potassium Silicate Drilling Fluid Affects Soil and Leachate Properties

In the research, in this paper, we investigate spatial and temporal variations in the composition of wastewater near Croatian highways in three climatic regions (continental, Mediterranean, highland) during three seasons (autumn, winter and spring). In our paper, the spatial division of the investigated areas that pertain to the three aforementioned climatic regions was obtained using the method of hierarchical clustering of monitored locations. One thousand five hundred thirty-three samples from 14 locations along Croatian highways were collected and analysed by methods of multivariate exploratory analysis. By methods of principal components, factor analysis and hierarchical clustering of variables, we grouped the variables into factors. Whereas 60 % of variation in the data was explained by three principal components, six principal components accounted for 88 % of data variation. The key section of our research was conducted by the decision tree method. For the purpose of analysis, we classified 1,533 samples into three classes representing climatic regions separately for each season and obtained the accuracy of 76–90 % on test samples. Finally, using decision trees, we identified the most important variables that differentiate climatic regions by the level of contamination of water along highways in different seasons.     

Uncertainties in long-term predictions of forest soil acidification due to neglecting seasonal variability

Soil and soil solution response simulated with a site-scale soil acidification model (NUCSAM) was compared with results obtained by a regional soil acidification model (RESAM). RESAM is a multi-layer model with a temporal resolution of one year. In addition to RESAM, NUCSAM takes seasonal variability into account since it simulates solute transport and biogeochemical processes on a daily basis. Consequently, NUCSAM accounts for seasonal variation in deposition, precipitation, transpiration, litterfall, mineralization and root uptake. Uncertainty caused by the neglect of seasonal variability in long-term predictions was investigated by a comparison of long-term simulations with RESAM and NUCSAM. Two deposition scenarios for the period 1990–2090 were evaluated. The models were parameterized and validated by using data from an intensively monitored spruce site at Solling, Germany. Although both the seasonal and the interannual variation in soil solution parameters were large, the trends in soil solution parameters of RESAM and NUCSAM corresponded quite well. The leaching fluxes were almost similar. Generally it appeared that the uncertainty due to time resolution in long-term predictions was relatively small.     

Review of modelling crop growth, movement of water and chemicals in relation to topsoil and subsoil compaction

Soil compaction influences crop growth, movement of water and chemicals in numerous ways. Mathematical modelling contributes to better understanding of the complex and variable effects. This paper reviews models for simulating topsoil and subsoil compaction effects. The need for including both topsoil and subsoil compaction results from still increasing compactive effect of vehicular pressure which penetrates more and more into the subsoil and which is very persistent. The models vary widely in their conceptual approach, degree of complexity, input parameters and output presentation. Mechanistic and deterministic models were most frequently used. To characterise soil compactness, the models use bulk density and/or penetration resistance and water content data. In most models root growth is predicted as a function of mechanical impedance and water status of soil and crop yield—from interactions of soil water and plant transpiration and assimilation. Models for predicting movement of water and chemicals are based on the Darcy/Richards one-dimensional flow equation. The effect of soil compaction is considered by changing hydraulic conductivity, water retention and root growth. The models available allow assessment of the effects of topsoil and subsoil compaction on crop yield, vertical root distribution, chemical movement and soil erosion. The performance of some models was improved by considering macro-porosity and strength discontinuity (spatial and temporal variability of material parameters). Scarcity of experimental data on the heterogeneity is a constraint in modelling the effects of soil compaction. Suitability of most models was determined under given site conditions. Few of the models (i.e. SIBIL and SIMWASER) were found to be satisfactory in modelling the effect of soil compaction on soil water dynamics and crop growth under different climate and soil conditions.     

沙河流域非点源溶解态氮负荷模拟及源解析

利用通用流域负荷模型(GWLF)对沙河流域2006—2012年的溶解态氮(DN)负荷进行了定量分析和来源解析,并基于模型分析了各污染源的季节性差异。模型在沙河流域适用性良好,月径流和DN负荷在校准期和验证期的ENS和R2都大于0.6,模型具备可靠的模拟能力。模拟结果表明,沙河流域DN年均负荷以非点源污染为主,占总污染负荷的82.7%。在所有土地利用类型中,耕地的DN负荷贡献率最大(64.5%),表明人类影响下的农业生产活动是流域非点源污染最主要的来源。同时,沙河流域DN负荷污染源组成表现出明显的季节性差异,丰水期地表径流是最主要的污染源(47.0%),而枯水期农村生活污染源(45.0%)和点源(23.1%)贡献相对显著。因此,在制定负荷削减方案时,应考虑氮污染源的季节性。     

流域非点源污染物输出的环境因子分析

通过流域断面水质监测和GIS的流域空间分析等手段建立石头口门水库流域基础数据库,运用多元统计分析和偏冗余度分析对枯水期和丰水期影响流域非点源污染物输出空间分异的自然环境、人为干扰、尺度因素及其交互作用等环境因子进行了定量分解,运用冗余分析方法探讨了两个时期不同环境因子对流域非点源污染物输出种类及其形态的影响。结果表明,所选环境因子在枯水期和丰水期分别解释了流域非点源污染水质空间变异的64．5％和68．2％;两个时期纯自然环境对非点源污染的影响均最为显著,分别解释了水质空间变异的43.7％和59．5％,其次是纯人为干扰,均超过了20％。自然环境－人为干扰的组合在枯水期较为强烈地影响非点源污染的产出,占到水质空间变异的20％以上,而在丰水期这一作用不明显。枯水期,以居民用地为主的人为干扰控制着流域内以氨氮和颗粒有机氮为主的氮的输出以及有机物质的输出,而以流域面积为主的尺度因素则主要影响可溶性磷和有机物质的输出。对于丰水期而言,非点源污染物输出的种类和形态受到不同类型环境因子组合的控制。     

Exploring soil databases: a self‐organizing map approach

A soil quality database (SQDB) is a collection of soil samples described by a given set of parameters, allowing farmers, scientists and other stakeholders to make informed decisions about practices, processes and policies for soil use and management. If each parameter is considered as a dimension of the space spanned by the SQDB, extracting information becomes a difficult task when the number of parameters is >3. A widely used approach to explore multidimensional data sets is the self‐organizing map (SOM) method, which is suitable for clustering, visualization and extraction of information from multidimensional data. We applied the SOM method as an exploratory technique to an unlabelled SQDB to extract knowledge – data patterns and data associations – from the data set (the time and location of each sample were unknown). The SQDB used in this study is a set of 1240 unlabelled samples within the Central Valley of Chile, covering ca 7500 km². The predominant soils are Andisols with a large organic matter content (7–12%), small bulk densities (0.6–1.0 g/cm³) and large water‐holding capacity. We identified three patterns: (i) isolated region within the map with close neurons (smooth transitions), (ii) two or more regions with predominantly large or small values and (iii) homogeneous map with small values with an isolated region of large values. These patterns show that the data set represented more than two groups that were not necessarily related. For pH, no important associations with other investigated parameters were observed. Previous studies carried out by the local agricultural research station showed that pH values below 5.5 constrain nutrient uptake. Thus, locations presenting pH<5,5 should be subject to seasonal monitoring to assess management practices that mitigate soil acidity. The component plane for organic matter indicates that ca. 50% of the soil samples had contents <8% related to soil series characteristics and management practices. As the k‐means is initialized by random partitions, the two‐step approach (clustering the map representing the input data) is less sensitive to variations in the input data (subsamples) than is the direct application of k‐means to the input data, but it also reduces the computational cost. The ability of SOMs to visualize multidimensional data sets helps gain an understanding of the data in the exploratory phase, such as the association and integration of physical, chemical and biological parameters.