Review: recent advances in biogas purifying technologies

Biomethane production through biogas upgrading is a promising renewable energy for some industries which could be part of the equilibrium needed with fossil fuels consumption to achieve a sustainable society. This paper presents a comprehensive list of biogas upgrading technologies focused on carbon dioxide removal as well as recent advances reported by researcher with wide expertise in this topic. Additionally, an extensive costs–performance comparison among the technologies studied is discussed. Among the different alternatives, chemical scrubbing stood out to achieve high biomethane purities while cryogenic technologies proved to be effective against methane losses. Regarding the different costs, water scrubbing and membrane separation seem to be the most affordable techniques.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

长春秋季生物质燃烧对PM2.5中WSOC吸光性的影响

为探究长春秋季生物质燃烧对PM_(2.5)中水溶性有机碳(water-soluble organic carbon,WSOC)吸光性的影响,于2017年10～11月进行PM_(2.5)样品采集,对PM_(2.5)中碳质组分、糖类化合物和WSOC的光吸收特征参数进行分析.研究表明:长春秋季PM_(2.5)中WSOC、有机碳(organic carbon,OC)、元素碳(elemental carbon,EC)的平均浓度分别为(10.12±3.47)、(17.07±5.64)和(1.34±0.75)μg·m~(-3),二次有机碳(secondary organic carbon,SOC)对OC的平均贡献率为38.93%.长春秋季总糖浓度为(1 049.39±958.85)ng·m~(-3),其中作为生物质燃烧示踪剂的脱水糖含量(左旋葡聚糖、半乳聚糖和甘露聚糖)在总糖中占比为91.69%,糖类相关性分析结果显示生物质燃烧源为长春秋季大气中糖类物质的主要贡献源.糖类物质的相关性分析及3种脱水糖的特征比值研究显示,作为长春秋季大气主要污染源的生物质燃烧的类型是硬木和作物残渣的燃烧.长春秋季WSOC的光吸收波长指数(AAE)为5.75±1.06,单位质量吸收效率(MAE)为(1.23±0.28)m~2·g~(-1),表明生物质燃烧对WSOC吸光性具有重要影响.利用生物质燃烧特征源参数量化计算生物质燃烧对WSOC浓度的贡献达58.82%,对总WSOC光吸收的贡献达40.92%.     

哈长城市群县域碳排放空间溢出效应及影响因素研究——基于NPP-VIIRS夜间灯光数据的实证

基于2012-2016年NPP-VIIRS夜间灯光数据测算哈长城市群共计68个县级单元碳排放数据,采用空间自相关、位序规模法则、空间计量模型以及空间马尔科夫链模型,对县级尺度城市碳排放空间特征、影响因素和动态空间溢出作用进行分析.结果表明：①城市碳排放呈现逐年下降的趋势,空间Moran''s I指数表明研究区城市碳排放存在高度的空间自相关,碳排放的高值集聚性呈降低的趋势.②位序规模法则表明,研究区全部城市的碳排放属于次位型分布,高位次城市的碳排放表现突出;在前十位城市的碳排放规模先减少再增加,由分散向集中演变.③多种空间面板模型对比分析表明,城市经济水平和人口密度因素呈现显著的正相关关系;固定投资和外商投资因素仅在时间固定模型中起到正向影响作用;技术进步以及路网密度因素则起到显著的抑制作用.④空间马尔科夫链分析结果表明,城市碳排放空间溢出效应明显,位于低水平区域与高水平区域的城市在转移过程中保持稳定;位于中低水平区域的城市与高碳排放的城市相邻会降低转移概率,反之则会提升转移概率.     

不同低碳氮比废水中好氧颗粒污泥的长期运行稳定性

为了研究好氧颗粒污泥系统处理低碳氮比废水的长期运行稳定性,采用低碳氮比（C/N）条件下逐步增加碳氮负荷的进水方法,分别在反应器A和B中接种好氧颗粒污泥,考察其长期运行过程中的理化性质、处理性能及应对冲击负荷的稳定性.其中A反应器的碳氮比一直维持在2,而B则由4逐步降至2.结果表明,在4℃存储30d的好氧颗粒污泥,经过25d的培养,其活性基本恢复,A、B反应器化学需氧量（COD）和氨氮（NH₄⁺-N）的去除效率均达到90%以上.在其后的稳定阶段,B反应器COD和NH₄⁺-N去除率达到90%以上,实现了完全硝化;而A反应器COD去除率仅80%左右,虽然NH₄⁺-N去除率最终也达到90%以上,但仅实现短程硝化.在冲击负荷阶段,A和B反应器COD去除率仍维持在80%以上,但是NH₄⁺-N去除受到很大冲击.A反应器NH₄⁺-N去除效率恶化,B反应器仅实现了部分硝化.整个运行过程,好氧颗粒污泥的物理性质受到的影响不大,A和B反应器的污泥容积指数（SVI₃₀）分别维持在60 mL ·g^-1和75 mL ·g^-1左右,混合液悬浮固体（MLSS）在5g ·L^-1和3.7g ·L^-1左右.颗粒污泥微生物群落分析表明,B反应器相对于A反应器丰富度和多样性更高.同时B反应器具有更高丰度的Zoogloea属,在颗粒中能产生更多的胞外蛋白促使颗粒结构更稳定,保证系统的长期稳定运行.以上结果表明,与C/N为2的好氧颗粒污泥系统相比,C/N为4的系统脱碳硝化效果好,抗冲击负荷能力强,更有利于颗粒污泥的长期稳定运行.     

降水空间异质性对非点源关键源区识别面积变化的影响

针对地形起伏和降水空间差异较大的农业区非点源污染问题,基于SWAT模型评估了阿什河流域在异质性降水和均匀降水两种情景下总氮、总磷关键源区空间变化规律,统计了两种情景下识别的关键源区面积变化,并分析其与降水特征参数的关系.结果表明,降水量一定时,两种情景下识别的总氮、总磷关键源区面积变化趋势大致相同,且总磷关键源区面积不易受降水空间异质性的影响,但总氮关键源区面积却明显受到其影响.对各年份总氮和总磷关键源区面积与降水特征参数的相关分析表明,总磷关键源区面积与当年降水量呈显著正相关,而总氮关键源区面积却与前一年降水量呈显著正相关.研究结果对进一步探讨降水这一重要驱动因子的不确定性对非点源污染关键源区的影响,以及农业非点源污染的治理具有重要意义.     

北京南部城区PM2.5中碳质组分特征

为了解《大气污染防治行动计划》实施后北京市大气PM_2.5中碳质组分特征,于2017年12月至2018年12月在北京污染较重的南部城区进行了PM_2.5连续采样,对其中的有机碳（OC）和元素碳（EC）进行了全面研究.结果表明,北京大气PM_2.5、OC和EC浓度变化范围分别为4.2~366.3、0.9~74.5和0.0~5.5 μg ·m^-3,平均浓度分别为（77.1±52.1）、（11.2±7.8）和（1.2±0.8）μg ·m^-3,碳质组分（OC和EC）整体占PM_2.5的16.1%.OC质量浓度季节特征表现为：冬季[（13.8±8.7）μg ·m^-3] > 春季[（12.7±9.6）μg ·m^-3] > 秋季[（11.8±6.2）μg ·m^-3] > 夏季[（6.5±2.1）μg ·m^-3],EC四季质量浓度水平均较低,范围为0.8~1.5 μg ·m^-3.二次有机碳（SOC）年均质量浓度为（5.4±5.8）μg ·m^-3,四季贡献比例范围为45.7%~52.3%,年均贡献为48.2%,凸显了二次形成的重要贡献.随污染加重,尽管OC和EC贡献比例均降低,但浓度水平却成倍升高,OC和EC浓度在严重污染天分别是空气质量为优天的6.3和3.2倍.与非供暖时段相比,供暖时段PM_2.5、OC和SOC浓度分别增加了14.4%、47.9%和72.1%,体现了OC对供暖季PM_2.5污染的重要贡献.PSCF分析表明,位于北京西南的山西省和河南省部分区域是PM_2.5和OC的主要潜在源区,且PM_2.5潜在源区更为集中;EC的PSCF高值（>0.7）区域较少,主要位于北京南部,如山东省和河南省部分地区,且北京市及周边地区贡献明显.     

基于绝对主成分-多元线性回归的滇池污染源解析

定量解析污染源是湖泊流域水环境管理的重要基础.基于滇池草海和外海多年水质监测数据,采用主成分分析（PCA）方法识别了主要水质指标的污染源类型,利用绝对主成分-多元线性回归模型（APCS-MLR）得到不同污染源对水质的贡献程度.结果表明,草海主要的污染源有农业面源、城市面源和内源3类,外海的主要污染源是农业面源、城镇生活污染源、城市面源和内源4类.与河流水污染源解析结果不同,底泥内源与气象因子对滇池主要水质指标的影响较大.     

基于LOADEST和数字滤波的水源地基流氮素输出定量方法应用实例

地表直接径流和基流均是流域非点源氮/磷养分输出的重要水文途径.科学认识和定量模拟基流氮/磷养分输出对于准确解析水源地水体非点源污染来源至关重要.基于Load Estimator模型和数字滤波算法,建立了定量水源地基流氮素输出的方法体系.以浙江省珊溪水源地的玉泉溪流域为例,利用玉泉溪2010-01—2013-12期间逐月总氮(TN)水质监测数据和逐日流量数据,展示了该方法的计算过程.结果表明,本文建立的水源地基流氮素输出定量方法结果合理,模拟精度高,决定系数和纳什系数分别为0.83和0.80;玉泉溪流域2010—2013年TN负荷量为141.21～274.68 t·a~(-1),平均208.63 t·a~(-1),年基流TN负荷量为84.39～168.68 t·a~(-1),平均127.69 t·a~(-1);基流对玉泉溪年均TN负荷量贡献率高达60%以上,流域基流养分输出对地表水体的污染应引起足够重视.     

基于改进遗传算法的河流水污染源反演方法

针对河流水污染应急响应过程中污染源排放历史迟知、未知的问题,结合多种群遗传算法和自适应遗传算法,利用一维河流水质模型和水质监测数据,研究建立了基于改进遗传算法的河流水污染定量源反演方法,实现了对河流污染源排放历史的识别与重构.将该方法应用于美国特拉基河流的3个不同流量下的示踪剂实验中,对示踪剂排放历史进行定量源反演分析.结果表明：IGA算法对高、中、低不同流量下的3次示踪剂实验均可以很好的重构和识别示踪剂排放历史,对于实际河流水污染源反演分析的误差均在可接受范围内.IGA算法在河流水污染源反演分析中具有一定的可靠性和稳定性,可为河流水污染精准溯源与治理提供科学的技术支撑.