Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Predicted no-effect concentrations for mercury species and ecological risk assessment for mercury pollution in aquatic environment

Mercury(Hg) exists in different chemical forms presenting varied toxic potentials. It is necessary to explore an ecological risk assessment method for different mercury species in aquatic environment. The predicted no-effect concentrations(PNECs) for Hg(Ⅱ) and methyl mercury(Me Hg) in the aqueous phase, calculated using the species sensitivity distribution method and the assessment factor method, were 0.39 and 6.5 × 10-3μg/L, respectively. The partition theory of Hg between sediment and aqueous phases was considered, along with PNECs for the aqueous phase to conduct an ecological risk assessment for Hg in the sediment phase. Two case studies, one in China and one in the Western Black Sea, were conducted using these PNECs. The toxicity of mercury is heavily dependent on their forms,and their potential ecological risk should be respectively evaluated on the basis of mercury species.     

Ozone concentrations, flux and potential effect on yield during wheat growth in the Northwest-Shandong Plain of China

Ozone (O₃) concentration and flux (F_o) were measured using the eddy covariance technique over a wheat field in the Northwest-Shandong Plain of China. The O₃-induced wheat yield loss was estimated by utilizing O₃ exposure-response models. The results showed that: (1) During the growing season (7 March to 7 June, 2012), the minimum (16.1 ppbV) and maximum (53.3 ppbV) mean O₃ concentrations occurred at approximately 6:30 and 16:00, respectively. The mean and maximum of all measured O₃ concentrations were 31.3 and 128.4 ppbV, respectively. The variation of O₃ concentration was mainly affected by solar radiation and temperature. (2) The mean diurnal variation of deposition velocity (V_d) can be divided into four phases, and the maximum occurred at noon (12:00). Averaged V_d during daytime (6:00–18:00) and nighttime (18:00–6:00) were 0.42 and 0.14 cm/sec, respectively. The maximum of measured V_d was about 1.5 cm/sec. The magnitude of V_d was influenced by the wheat growing stage, and its variation was significantly correlated with both global radiation and friction velocity. (3) The maximum mean F_o appeared at 14:00, and the maximum measured F_o was − 33.5 nmol/(m²·sec). Averaged F_o during daytime and nighttime were − 6.9 and − 1.5 nmol/(m²·sec), respectively. (4) Using O₃ exposure-response functions obtained from the USA, Europe, and China, the O₃-induced wheat yield reduction in the district was estimated as 12.9% on average (5.5%–23.3%). Large uncertainties were related to the statistical methods and environmental conditions involved in deriving the exposure-response functions.     

Effect of AlCl3 concentration on nanoparticle removal by coagulation

In recent years, engineered nanoparticles, as a new group of contaminants emerging in natural water, have been given more attention. In order to understand the behavior of nanoparticles in the conventional water treatment process, three kinds of nanoparticle suspensions, namely multi-walled carbon nanotube-humic acid (MWCNT-HA), multi-walled carbon nanotube-N,N-dimethylformamide (MWCNT-DMF) and nanoTiO₂-humic acid (TiO₂-HA) were employed to investigate their coagulation removal efficiencies with varying aluminum chloride (AlCl₃) concentrations. Results showed that nanoparticle removal rate curves had a reverse “U” shape with increasing concentration of aluminum ion (Al^3 +). More than 90% of nanoparticles could be effectively removed by an appropriate Al^3 + concentration. At higher Al^3 + concentration, nanoparticles would be restabilized. The hydrodynamic particle size of nanoparticles was found to be the crucial factor influencing the effective concentration range (ECR) of Al^3 + for nanoparticle removal. The ECR of Al^3 + followed the order MWCNT-DMF > MWCNT-HA > TiO₂-HA, which is the reverse of the nanoparticle size trend. At a given concentration, smaller nanoparticles carry more surface charges, and thus consume more coagulants for neutralization. Therefore, over-saturation occurred at relatively higher Al^3 + concentration and a wider ECR was obtained. The ECR became broader with increasing pH because of the smaller hydrodynamic particle size of nanoparticles at higher pH values. A high ionic strength of NaCl can also widen the ECR due to its strong potential to compress the electric double layer. It was concluded that it is important to adjust the dose of Al^3 + in the ECR for nanoparticle removal in water treatment.     

河流氧化亚氮产生和排放研究综述

氧化亚氮(N2O)是仅次于二氧化碳(CO2)和甲烷(CH4)的重要温室气体.由于人类活动对土地的影响导致河流系统中氮的可利用性增加,河流生态系统的N2O排放量正日益增长.本文对国内外河流水体N2O溶存浓度和饱和度、水-气界面排放通量及沉积物-水界面交换通量等数据进行了收集,并总结和分析了河流生态系统中N2O的产生机制及主要影响因子.     

上海市降水过程中汞的浓度变化研究

为了研究降水过程中汞浓度变化规律,采集了上海市区2009年7月6日一次连续降水过程中的分时段12个雨水样品,利用冷原子荧光测汞仪测定了总汞、溶解态汞和颗粒态汞浓度.分析结果显示,降水过程各时段雨水样品的总汞浓度变化范围为0.26～0.53 μg·L-1之间,其中溶解态汞占总汞的比例为67.9％～96.8％;总汞浓度和颗粒态汞浓度的总体变化规律较一致,降水事件的前30分钟内浓度较高,之后逐渐降低,最终趋于稳定;溶解态汞浓度从降水事件开始由高到低逐渐变化,最终也趋于稳定.通过清除比计算云下冲刷过程对雨水中总汞浓度的贡献,发现云下冲刷的贡献大于40.9％.     

乌鲁木齐市民用住宅室内氡浓度水平调查研究

对乌鲁木齐市具有代表性的民用住宅室内氡浓度进行了连续一年的监测调查,调查结果显示室内氡浓度平均值55.4 Bq／m3,低于国标的“200 Bq／m3”,并据此估算出其所致居民的年均有效剂量为1.400 mSv,以及所致居民支气管上皮组织、肺、性腺、骨髓及骨表面细胞等关键组织的年吸收剂量分别为1886．49,378.96,2.80,3.20,3.20μGy／a,大于UNSCEAR2000年报告的世界平均值1.008 mSv,结合室内氡的主要来源,提出了对建筑材料进行放射性监测、控制装修、工程竣工后开展室内环境监测、加强室内通风以及开展室内氡污染治理等措施控制室内氡浓度水平。     

恶臭污染评价方法及来源识别研究

随着经济的发展,人们的环境意识不断增强,恶臭作为一种环境污染已越来越受到人们的重视.文章概述了恶臭评价的主要影响因素,以及目前常用的评价方法如臭气强度评价法、臭气浓度(或臭气指数)评价法、臭气扩散模型预测评价法,同时从谱图恶臭污染源识别和源解析模型识别技术两方面介绍了恶臭污染物源解析技术,为恶臭污染事故诊断分析、定性恶臭污染物来源提供了一种新的途径和方法.最后通过分析目前尚未解决的科学问题,指出了今后的研究方向.     

基于两种样品前处理方法对比研究小冬克玛底冰川雪坑中痕量元素

通过对唐古拉山小冬克玛底冰川雪坑中非季风季节沉积的雪样分别进行酸化处理和消解处理后,利用高分辨扇形磁场等离子体质谱仪(ICP-SFMS)测试了样品中19种痕量元素(Ba、U、Sr、Rb、Tl、Mo、Cs、Pb、Sb、V、Cr、Mn、Fe、Co、Al、Cu、Ti、Li、As)的酸化浓度和总浓度.研究结果表明,痕量元素浓度的变化范围较大,元素Al的最大/最小浓度比为326(酸化浓度)和465(总浓度),元素Pb相应比值为27和48.雪冰中痕量元素的总浓度一般大于该元素的酸化浓度,其中,元素Pb、Fe、Sb、Ba、Al、Ti的酸化浓度占总浓度的平均比值分别为91%、76%、60%、52%、33%和21%.一般地,样品中不溶微粒含量越大,酸化浓度占总浓度的比值越小;不溶微粒含量越小,则相反.对痕量元素的富集系数(EF)分析表明,各元素总浓度EF均值小于酸化浓度EF均值,揭示了用酸化浓度计算EF存在对痕量元素人为来源影响的高估.人类排放是小冬克玛底冰川中痕量元素的来源之一,对于元素Mo和Sb,人类排放估计是主要来源.利用后向轨迹模型模拟出小冬克玛底冰川雪冰中痕量元素在非季风季节主要来源于青藏高原西部及中亚中东地区.     

Evaluating farm performance using agri-environmental indicators: recent experiences for nitrogen management in The Netherlands

Intensive agriculture, characterized by high inputs, has serious implications on the environment. Monitoring and evaluation of projects aiming at designing, testing and applying more sustainable practices require instruments to asses agronomic as well as environmental performance. Guidelines for Good Agricultural Practice (GAP) or Good Farming Practice (GFP) define sustainable practices but give limited insight into their environmental performance. Agri-environmental indicators (AEIs) provide information on environmental as well as agronomic performance, which allows them to serve as analytical instruments in research and provide thresholds for legislation purposes. Effective AEIs are quantifiable and scientifically sound, relevant, acceptable to target groups, easy to interpret and cost-effective. This paper discusses application of four AEIs for nitrogen (N) management in three Dutch research projects: 'De Marke', 'Cows and Opportunities' and 'Farming with a future'. 'De Marke' applied Nitrogen Surplus and Groundwater Nitrate Concentration in the design and testing of environmentally sound dairy systems. 'Cows and Opportunities', testing and disseminating dairy systems designed at 'De Marke', mainly applied Nitrogen Surplus, while 'Farming with a future' used Nitrogen Surplus, Groundwater Nitrate Concentration and Residual Mineral Soil Nitrogen to support arable farmers in complying with Dutch legislation (MINAS). Nitrogen Surplus is quantifiable, appealing and easy to interpret, but lacks scientific soundness or a good relationship with groundwater quality. Nitrogen Use Efficiency is sensitive to changes in management, while Residual Mineral Soil Nitrogen is appealing and cheap, but has difficulties in scaling. Groundwater Nitrate Concentration lacks clear rules for sampling, is labor consuming, expensive and mainly used in combination with other indicators. AEIs enhanced improvements in N management by facilitating (i) definition of project goals, (ii) design of desired systems, (iii) evaluation of applied systems and (iv) improving effective communication. AEI applications in other countries show a similar pattern as found in The Netherlands. Limitations to AEI application relate to inconsistencies between different indicators, heterogeneity of soil characteristics and linkages of N, carbon and water management. AEIs should be applied in an integrated evaluation, at a scale that reflects the farm's spatial variability. Simple AEIs like Nitrogen Surplus should be supported by other indicators and/or model calculations. The paper concludes that AEIs proved their value in design, implementation and testing of farming systems, but they should be used with care, always keeping in mind that indicators are simplifications of complex and variable processes.