利用悬浮填料附着生物膜同步去除碳氮磷

采用移动床生物膜反应器（MBBR）处理配制模拟废水，实验结果表明，水力停留时间为6h、悬浮填料填充率为40％时，在不同C／N／P比率条件下，MBBR对COD、NH4＋-N和TN去除性能好且稳定，平均去除率分别达到90％、94．8％和62．39％以上，而TP的去除率受C／N／P值影响较大，当C／N／P的比值为100／10／1．8时，平均去除率达到58．03％。一定的溶解氧（DO）质量浓度能保证反应器中COD、NH4-N高效稳定的去除，同时是TN和TP同时去除的重要影响因素，在MBBR中最佳DO值约为3mg／L。由于附着在悬浮填料生物膜内部存在厌氧、缺氧微环境条件，在反应器中存在少量的反硝化聚磷菌。     

Organic pollution of the Büyük Menderes River,Turkey and effects on aquaculture

Water quality was measured at eight stations on the Buyuk Menderes River in Turkey (Ad?güzel dam, Yenice regülator, Sarayköy bridge, Feslek regülator, Yenipazar bridge, Ayd?n bridge, Koçarl? bridge, Söke regülator) between 2000 and 2013 in February, April, June, August, October and December. The resulting data were evaluated in terms of biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), ammoniac–nitrogen (NH₃–N), nitrite–nitrogen (NO₂–N), nitrate–nitrogen (NO₃–N) and orthophosphate (o-PO₄) aquaculture. According to the analysis, while river water pollution generally varied during each year, samples from certain measurement points demonstrated high pollution levels throughout the year. In this study, water parameters were classified according to the “Turkish water pollution control regulation.” The studied parameters are also compared with TS 266 and WHO guidelines. While levels at Sarayköy station were generally higher than other stations, values at Ad?güzel dam were the lowest, giving it the best water quality of the eight stations. The highest values on a yearly basis were obtained in 2007 due to the severe drought in the Menderes basin within which irrigation water levels fell to 4255 m³/ha. The BOD, COD levels are the lowest in 2009 and highest in 2007; the DO level is lowest in 2007 and highest in 2009; NH₃–N, NO₂–N, and NO₃–N parameters are the lowest in 2007 and highest in 2009; and the o-PO₄ are at the lowest level in 2004 and seen as the highest in 2007. Analysis of the data was performed by SPSS 21 statistics program. One direction ANOVA was applied to the data, which were also subject to Tukey multiple comparison tests. Differences between groups were evaluated at p?<?0.05. Box–plot graphs were used to demonstrate the data distribution. In the study, it was analyzed, the effect of fish species and pollution involved in the Büyük Menderes River so far on fish species.     

生物脱氮除磷活性污泥系统复合模拟方法

为避免繁琐的参数校核工作,提出了活性污泥2 d号模型(ASM2d)和人工神经网络(ANNs)相结合的复合模拟方法。考察了复合方法在某污水处理厂生物脱氮除磷工艺中的应用情况。研究表明,ANNs能够准确地模拟出水实测值与未经校核的ASM2d机理模型的估计值之间的差值。利用Levenberg-Marquardt算法,对出水氨氮、总氮和总磷分别建立网络结构为5-12-1、5-8-1和5-8-1的ANNs子模型,将这些子模型输出同ASM2d机理模型输出相加便得到复合模型输出。复合模型估计值对前10.4 d(ANNs子模型训练数据时段)出水氨氮、总氮和总磷浓度的拟合平均绝对百分比误差分别为0.267、0.055和0.048;其对后2.6 d(ANNs子模型测试数据时段)出水氨氮、总氮和总磷浓度的预测平均绝对百分比误差分别为0.332、0.083和0.069。均方根误差、平均绝对误差等评价指标也表明复合模型能够给出合理的模拟结果。     

Nitrification/denitrification in an intermittent aeration process for swine wastewater

Abstract

Nitrogen removal by a methane fermentation plus activated sludge process with the intermittent aeration was presented based upon a full‐scale pig farm experiment. Swine wastewater had a T‐N/BOD ratio from 0.2–0.29. The BOD concentration input to the process ranged from 1050–1608 mg l^‐1 and the T‐N concentration from 273–350 mg l^‐1. More than 90% of organic carbon was removed in each experimental run. Only small concentrations of NO₃‐N were found in the effluent and higher than 60% of the T‐N and 73% of NH₄‐N which were loaded to the intermittent aeration tank was removed. The nitrogen balance of each run was calculated. Denitrification was estimated to be accountable for 45–90% of the T‐N removed in the intermittent aeration tank. Denitrification rate increased as the BOD concentration increased (> 1300 mg l^‐1). The T‐N removal percentage was a function of the T‐N/BOD ratio of the influent. Although higher DO concentration (> 3 mg l^‐1) did not enhance the denitrification rate, nitrification did maintain at relative higher rates at a lower DO concentration (ave. 1.5 mg l^‐1). An operational condition of intermittent interval of aeration/nonaeration at 1:1 hr is better than that of the condition at 3:1 hr. As a result, T‐N and NH4‐N were removed up to 30% and 40% respectively, and the denitrification rate reached 79% in the intermittent aeration tank. An experimental run in a pilot plant on treating anaerobically pretreated swine wastewater was observed to verify simultaneous nitrification/denitrification. The results of the full‐scale plant experimentation seem to be confirmed by those obtained from the pilot plant runs.     

Elevated nitrogen isotope ratios of tropical Indian aerosols from Chennai: Implication for the origins of aerosol nitrogen in South and Southeast Asia

To better understand the origins of aerosol nitrogen, we measured concentrations of total nitrogen (TN) and its isotope ratios (δ¹⁵N) in tropical Indian aerosols (PM₁₀) collected from Chennai (13.04°N; 80.17°E) on day- and night-time basis in winter and summer 2007. We found high δ¹⁵N values (+15.7 to +31.2‰) of aerosol N (0.3–3.8 μg m^?3), in which NH₄⁺ is the major species (78%) with lesser contribution from NO₃^? (6%). Based on the comparison of δ¹⁵N in Chennai aerosols with those reported for atmospheric aerosols from mid-latitudes and for the particles emitted from point sources (including a laboratory study), as well as the δ¹⁵N ratios of cow-dung samples (this study), we found that the atmospheric aerosol N in Chennai has two major sources; animal excreta and bio-fuel/biomass burning from South and Southeast Asia. We demonstrate that a gas-to-particle conversion of NH₃ to NH₄HSO₄ and (NH₄)₂SO₄ and the subsequent exchange reaction between NH₃ and NH₄⁺ are responsible for the isotopic enrichment of ¹⁵N in aerosol nitrogen.     

垃圾渗滤液处理工艺运行参数优化与技术比较

垃圾渗滤液是公认的一种成分复杂且难以处理的高浓度有机废水,笔者在北京市海淀区六里屯垃圾填埋场通过2007—2010年垃圾渗滤液处理的工程实践,以COD和氨氮的去除率为指标,研究了不同的垃圾渗滤液处理工艺组合,以及不同运行参数条件下对垃圾渗滤液的处理效果。结果表明,在中温UASB和A/O的平均水力停留时间(HRT)缩短1/3的情况下,通过改进A/O段曝气方式,优化系统的pH、DO等运行参数,用MBR替代絮凝工艺,使整个组合工艺对COD的年平均去除率达到了94.3%,氨氮的去除率维持在99.5%以上,出水氨氮稳定在10 mg/L以内,而改造前的COD与氨氮的年平均去除率仅为82.2%与55.3%。与改造前的UASB+A/O+絮凝工艺组合相比,改造后的UASB+A/O+MBR工艺组合具有更高的污染物去除能力、更好的抗缓冲性和稳定性。     

Ammonia emissions from naturally ventilated dairy cattle buildings and outdoor concrete yards in Portugal

There is a lack of information on ammonia (NH₃) emissions from cattle housing systems in Mediterranean countries, with most published data deriving from NW Europe. An investigation was carried out in NW Portugal to quantify NH₃ emissions for the main types of dairy cattle buildings in Portugal, i.e. naturally ventilated buildings and outdoor concrete yards, and to derive robust emission factors (EFs) for these conditions and compare with EFs used elsewhere in Europe. Measurements were made throughout a 12-month period using the passive flux sampling method in the livestock buildings and the equilibrium concentration technique in outdoor yards.The mean NH₃ emission factor for the whole housing system (buildings + outdoor yards) was 43.7 g NH₃–N LU^?1 day^?1 and for outdoor concrete yards used by dairy cattle was 26.6 g NH₃–N LU^?1 day^?1. Expressing NH₃ emission in terms of the quantity of liquid milk produced gave similar values across the three dairy farms studied (with a mean of 2.3 kg N ton-milk^?1 produced) and may have advantages when comparing different farming systems. In dairy houses with outdoor yards, NH₃ emissions from the yard area contributed to 69–92% of total emissions from this housing system. Emissions were particularly important during spring and summer seasons from outdoor yards with NH₃ emitted in this period accounting for about 72% of annual emissions from outdoor yards. Mean NH₃ emission factors derived for this freestall housing system and outdoor concrete yards used by dairy cattle in Portugal were higher than those measured in northern Europe. In addition, values of animal N excretion estimated in this study were greater than official National standard values. If these emissions are typical for Portuguese dairy systems, then the current National inventory underestimates emissions from this source in NW of Portugal, because of the use of lower standard values of N excretion by dairy cattle.     

Adsorption of ammonium on biochar prepared from giant reed

Giant reed was used as precursor for making biochar in order for the adsorption of NH₄ ⁺–N from aqueous solution. And the adsorption of the product to NH₄ ⁺–N was examined. The surface features of biochar were investigated by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy/energy dispersive spectrometer (SEM-EDS), and X-ray diffraction (XRD). XRD patterns showed several peaks and correspond to the high amount of crystalline material. The crystals contain KCl, K₂O, CaO, MgO, and SiO and possess high surface area which enhances adsorption. The influence of different parameters such as initial concentration, adsorption time, pH, and ionic strength has been carried out. The adsorption could reach equilibrium through 24 h reaction and had the best adsorption amount at the solution pH values from 7 to 9. The cation has great influence on the adsorption of NH₄ ⁺–N, whereas the anion exerted a weaker effect. The adsorption followed pseudo-first-order and pseudo-second-order models. And the intraparticle diffusion and desorption studies further elucidated that the mechanism of adsorption on the product was ion exchange. The product equilibrium data was well described by the Langmuir and Freundlich model. The maximum adsorption capacities were 1.490 mg/g. Biochar derived from giant reed at 500 °C was suggested as a promising adsorbent for the removal of NH₄ ⁺–N from slightly polluted wastewater.     

Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study

The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN > GRNN > BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model with the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than ±10 %. In case of the MLR, only 55 % of predictions were within the error of less than ±10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters.     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Size partitioning of particulate inorganic nitrogen species between the fine and coarse mode ranges and its implication to their deposition on the surface ocean

In order to discuss the dry deposition fluxes of atmospheric fixed nitrogen species, observations of aerosol chemistry including nitrate (NO₃^?) and ammonium (NH₄⁺) were conducted at two islands, Rishiri Island and Sado Island, over the Sea of Japan. Although the atmospheric concentrations of particulate NH₄⁺–N showed higher values than those of particulate NO₃^?–N at both sites, the dry deposition fluxes of the particulate NO₃^?–N were estimated to be higher than those of the particulate NH₄⁺–N. This was caused by the difference of particle sizes between the particulate NO₃^? and NH₄⁺; NH₄⁺ was almost totally contained in fine particles (d < 2.5 μm) with smaller deposition velocity, whereas NO₃^? was mainly contained in coarse particles (d > 2.5 μm) with greater deposition velocity. Fine mode NO₃^? was strongly associated with fine mode sea-salt and mineral particles, of which higher concentrations shifted the size of particulate NO₃^? toward the fine mode range. This size shift would decrease the dry deposition flux of the fixed nitrogen species on coastal waters and accelerate atmospheric transport of them to the remote oceanic areas.     

Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring

We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO₃) concentration, and ammoniacal nitrogen concentration (NH₃-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO₃) or oxygen demand (NH₃-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R ²), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.     

Temporal variability of soil-atmospheric CO2 and CH4 fluxes from different land uses in mid-subtropical China

Different land uses in subtropics play an important role in regulating the global environmental changes. To reduce uncertainties of greenhouse gas (GHG) emissions of agricultural soils in subtropical ecosystem, a four years campaign was started to determine the temporal GHG (CO₂ and CH₄) fluxes from seven sites of four land use types (1 vegetable field, 3 uplands, 2 orchards, 1 pine forest). The mean annual budgets of CO₂, and CH₄ were 6.5～10.5 Mg CO₂ ha^?1 yr^?1, and +0.47 ～ ?2.37 kg CH₄ ha^?1 yr^?1, respectively. Pine forest had significantly lower CO₂ emission and higher CH₄ uptake than agriculture land uses. Tilled orchard emitted more CO₂ and oxidized less CH₄ than non-tilled orchard. Upland crops had higher CO₂ emissions than orchards, while abrupt differences of CH₄ uptake were observed between upland crops and orchards. Every year, the climate was warm and wet from April to September (the hot–humid season) and became cool and dry from October to March (the cool–dry season). Driven by seasonality of temperature and WFPS, CO₂ fluxes were significantly higher in the hot–humid season than in cool–dry season. Soil temperature, WFPS, NO₃^?–N and NH₄⁺–N contents interactively explained CH₄ uptake which was significantly higher in cool–dry season than in hot–humid season. We conclude that soil C fluxes from different land uses are strongly under control of different climatic predictors along with soil nutrient status, which interact in conjunction with each other to supply the readily available substrates.     

Effects of high ammonium level on biomass accumulation of common duckweed Lemna minor L.

Growing common duckweed Lemna minor L. in diluted livestock wastewater is an alternative option for pollutants removal and consequently the accumulated duckweed biomass can be used for bioenergy production. However, the biomass accumulation can be inhibited by high level of ammonium (NH₄ ⁺) in non-diluted livestock wastewater and the mechanism of ammonium inhibition is not fully understood. In this study, the effect of high concentration of NH₄ ⁺ on L. minor biomass accumulation was investigated using NH₄ ⁺ as sole source of nitrogen (N). NH₄ ⁺-induced toxicity symptoms were observed when L. minor was exposed to high concentrations of ammonium nitrogen (NH₄ ⁺-N) after a 7-day cultivation. L. minor exposed to the NH₄ ⁺-N concentration of 840 mg l^?1 exhibited reduced relative growth rate, contents of carbon (C) and photosynthetic pigments, and C/N ratio. Ammonium irons were inhibitory to the synthesis of photosynthetic pigments and caused C/N imbalance in L. minor. These symptoms could further cause premature senescence of the fronds, and restrain their reproduction, growth and biomass accumulation. L. minor could grow at NH₄ ⁺-N concentrations of 7–84 mg l^?1 and the optimal NH₄ ⁺-N concentration was 28 mg l^?1.     

Ammonia volatilization from sows on grassland

According to regulations, sows with piglets on organic farms must graze on pastures. Volatilization of ammonia (NH₃) from urine patches may represent a significant source of nitrogen (N) loss from these farms. Inputs of N are low on organic farms and losses may reduce crop production. This study examined spatial variations in NH₃ volatilization using a movable dynamic chamber, and the pH and total ammoniacal nitrogen (TAN) content in the topsoil of pastures with grazing sows was measured during five periods between June 1998 and May 1999. Gross NH₃ volatilization from the pastures was also measured with an atmospheric mass balance technique during seven periods from September 1997 until June 1999. The dynamic chamber study showed a high variation in NH₃ volatilization because of the distribution of urine; losses were between 0 and 2.8 g NH₃–N m⁻² day⁻¹. Volatilization was highest near the feeding area and the huts, where the sows tended to urinate. Ammonia volatilization rate was linearly related to the product of NH₃ concentration in the boundary layer and wind speed. The NH₃ in the boundary layer was in equilibrium with NH₃ in soil solution. Gross NH₃ volatilization was in the range 0.07–2.1 kg NH₃–N ha⁻¹ day⁻¹ from a pasture with 24 sows ha⁻¹. Ammonia volatilization was related to the amount of feed given to the sows, incident solar radiation and air temperature during measuring periods, and also to temperature, incident solar radiation and rain 1–2 days before measurements. Annual ammonia loss was 4.8 kg NH₃–N sow⁻¹.     

Redundancy analysis for characterizing the groundwater quality in coastal industrial areas

Groundwater quality in coastal area has been an issue of interest because of excessive groundwater extraction for human use, for example, industrialization, irrigation, which can lead to saltwater intrusion. The study develops an integrated data analysis procedure based on multivariate statistics principal component analysis (PCA), hierarchical cluster analysis (HCA) and redundancy analysis (RDA), to determine the effects of key environmental conditions on the formulation of groundwater pollutants. This proposed method was demonstrated by analyzing groundwater quality monitoring data collected between 2011 and 2014 from four coastal industrial areas in Changhua county of Taiwan, namely Chuansing, Xianxi, Lukang and Fangyuan industrial parks. First, different environmental conditions in each industrial region were explored by PCA. The spatial hierarchy and spatial distribution of pollutant categories were then identified using HCA with the kriging method. Finally, the effect of environmental conditions on constitutive pollutants were identified with RDA. The three environmental patterns identified from the analytical results in Chuansing, Lukang and Xianxi were the salination factor (including conductivity and general hardness (GH)), water level and redox condition (including dissolved oxygen and oxidation–reduction potential). Fangyuan industrial park had only two patterns, namely salination (including conductivity and GH) and oxygen content (including DO and depth). The pollutant category indicated high concentrations of all pollutants in Chuansing and Fangyuan, and higher concentration of SO₄^2?, TDS, Cl^? in Xianxi, and of NH₃-N, Mn, Fe and TOC in Lukang. According to RDA results, salination caused the high concentrations of NH₃N, Cl^?, TDS in Chuansing, and of Cl^?, TDS and SO₄^2? in Xianxi and Lukang. Additionally, salination caused high concentrations of Fe in both Lukang and Fangyuan industrial parks in combination with those three pollutants. The redox condition was linked to high content of NO₃^? in Chuansing and Lukang, and of TOC in Xianxi. In Fangyuan industrial park, NO₃^? was also linked to high oxygen concentration. In summary, the combination of PCA, HCA and RDA enables the analysis of monitoring data to support policy decision-making.     

Combined effects of nitrogen addition and organic matter manipulation on soil respiration in a Chinese pine forest

The response of soil respiration (Rs) to nitrogen (N) addition is one of the uncertainties in modelling ecosystem carbon (C). We reported on a long-term nitrogen (N) addition experiment using urea (CO(NH₂)₂) fertilizer in which Rs was continuously measured after N addition during the growing season in a Chinese pine forest. Four levels of N addition, i.e. no added N (N0: 0 g N m⁻² year⁻¹), low-N (N1: 5 g N m⁻² year⁻¹), medium-N (N2: 10 g N m⁻² year⁻¹), and high-N (N3: 15 g N m⁻² year⁻¹), and three organic matter treatments, i.e. both aboveground litter and belowground root removal (LRE), only aboveground litter removal (LE), and intact soil (CK), were examined. The Rs was measured continuously for 3 days following each N addition application and was measured approximately 3–5 times during the rest of each month from July to October 2012. N addition inhibited microbial heterotrophic respiration by suppressing soil microbial biomass, but stimulated root respiration and CO₂ release from litter decomposition by increasing either root biomass or microbial biomass. When litter and/or root were removed, the “priming” effect of N addition on the Rs disappeared more quickly than intact soil. This is likely to provide a point of view for why Rs varies so much in response to exogenous N and also has implications for future determination of sampling interval of Rs measurement.

     

Canopy leaching of nutrients and metals in a mountain spruce forest

Precipitation and throughfall fluxes of major ions, nutrients (C, N, P), and metals (Al, Fe, Mn), and the chemical composition of litter fall and living plant tissue in Norway spruce stands (the Bohemian Forest; Czech Republic), were used to evaluate how microbial processes and decay of plant tissue in canopies influence canopy leaching (CL) of elements. Proton exchange for Mg²⁺, Ca²⁺, and K⁺ in decaying biomass and co-transport of Ca²⁺ and K⁺ out of plant cells with organic acid anions were the most likely processes contributing to CL of base cations. The CL of total P and N (and also NO₃^?) was minor. Important proportions of the N and P mineral forms were transformed to organic forms by microbial processes (primary and bacterial production), with the respective CL of ?13.9 and 16.4 mmol m^?2 yr^?1 for NH₄⁺ and organic N, and ?0.33 and 0.22 mmol m^?2 yr^?1 for dissolved reactive P (DRP) and organic P. Most of particulate P and N in throughfall (～90%) originated from microbial DRP and NH₄⁺ transformations, but particulate C mostly came from the fragmentation of plant tissue (58%). Among metals, CL was not observed for Al, was small for Fe (0.3 mmol m^?2 yr^?1), and greatest for Mn (0.9 mmol m^?2 yr^?1) due to leaching from decaying tissue by acidic precipitation.     

Behaviour and dynamics of di-ammonium phosphate in bauxite processing residue sand in Western Australia—I. NH<Subscript>3</Subscript> volatilisation and residual nitrogen availability

Background, aim and scope

Australia is the largest producer of bauxite in the world, with an annual output of approximately 62 million metric dry tons in 2007. For every tonne of alumina, about 2 tonnes of highly alkaline and highly saline bauxite-processing residue are produced. In Western Australia, Alcoa World Alumina, Australia (Alcoa) produces approximately 15 MT of residue annually from its refineries (Kwinana, Pinjarra and Wagerup). The bauxite-processing residue sand (BRS) fraction represents the primary material for rehabilitating Alcoa’s residue disposal areas (RDAs). However, the inherently hostile characteristics (high alkalinity, high salinity and poor nutrient availability) of BRS pose severe limitations for establishing sustainable plant cover systems. Alcoa currently applies 2.7 t ha^?1 of di-ammonium phosphate ((NH₄)₂HPO₄; DAP)-based fertiliser as a part of rehabilitation of the outer residue sand embankments of its RDAs. Limited information on the behaviour of the dominant components of this inorganic fertiliser in highly alkaline BRS is currently available, despite the known effects of pH on ammonium (NH₄) and phosphorus (P) behaviour. The aim of this study was to quantify the effects of pH on NH₃ volatilisation and residual nitrogen (N) in BRS following DAP applications.

Methods

The sponge-trapping and KCl-extraction method was used for determining NH₃ volatilisation from surface-applied DAP in samples of BRS collected from each of Alcoa’s three Western Australia Refineries (Kwinana, Pinjarra, Wagerup) under various pH conditions (pH 4, 7, 9 and 11). Following cessation of volatilisation, the residual N was extracted from BRS using 2 M KCl and concentrations of NH ₄ ⁺ –N and NO ₃ ^? –N were determined by flow injection analysis.

Results

The quantities of NH₃ volatilised increased dramatically as the pH increased from 4 to 11. Much of the N lost as NH₃ (up to 95.2%) occurred within a short period (24 h to 7 days), particularly for the pH 9 and 11 treatments. Concentrations of residual NH ₄ ⁺ –N recovered in DAP-treated BRS at the end of the experiment decreased with increasing pH. This finding was consistent with increasing loss of N via volatilisation as pH increased. The concentration of NO ₃ ^? –N was very low due to no nitrification in BRS.

Discussion

The pH was a key driver for NH₃ volatilisation from DAP-treated BRS and primarily controlled N dynamics in BRS. Results indicate that NH₄ not adsorbed by BRS was highly susceptible to volatilisation. The likely lack of nitrifying bacteria did not allow conversion of ammonium to nitrate, thereby further exacerbating the potential for loss via volatilisation

Conclusions

It was demonstrated that the pH is the key factor controlling the loss of inorganic N from BRS. Although volatilisation was considerably lower at pH 4, achieving this pH reduction in the field is not possible at present. Findings from this study highlight the need to better understand which forms of N fertiliser are most suitable for use in highly alkaline BRS.

Recommendation and perspectives

Although pH reduction is the most likely means of stopping NH₃ volatilisation in BRS, it is economically and operationally unfeasible to add sufficient acidity for adequately lowering pH in the BRS for revegetation. More attention on forms of fertilisers more suitable to highly alkaline, microbially inert soil conditions appears to be warranted.

     

Determination of Atmospheric Nitrogen Input to Lake Greenwood,South Carolina: Part 2—Gaseous Measurements and Modeling

Abstract

The Reedy River branch of Lake Greenwood, SC, has repeatedly experienced summertime algal blooms, upsetting the natural system. A series of experiments were carried out to investigate atmospheric nitrogen (N) input into the lake. N was examined because of the insignificant phosphorus dry atmospheric flux and the unique nutrient demands of the dominant algae (Pithophora oedogonia) contributing to the blooms. Episodic atmospheric measurements during January and March 2001 have shown that the dry N flux onto the lake ranged from 0.9 to 17.4 kg N/ha-yr, and on average is caused by nitric acid (HNO₃; 31%), followed by nitrogen dioxide (NO₂; 23%), fine ammonium (NH₄ ⁺; 20%), coarse nitrate (NO₃ ^?; 16%), fine NO₃ ⁺ (5%), and coarse NH₄ ⁺ (5%). Similar measurements in Greenville, SC (the upper watershed of the Reedy River), showed that the dry N deposition flux there ranged from 1.4 to 9.7 kg N/ha-yr and was mostly caused by gaseous deposition (40% NO₂ and 40% HNO₃). The magnitude of this dry N deposition flux is comparable to wet N flux as well as other point sources in the area. Thermodynamic modeling showed low concentrations of ammonia, relative to the particulate NH₄ ⁺ concentrations.