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     

The Impact of Agriculture On Dissolved Nitrogen and Phosphorus Cycling in Temperate Ecosystems

Although most research has focussed on inorganic nutrient forms of nitrate (NO^-₃) and phosphorus (PO³₄) in runoff and receiving waters, nitrogen loss from agricultural land can also occur in organic and ammonium-nitrogen form; phosphorus losses, although often dominated by particulate transport, may occur in soluble organic and inorganic form. Furthermore, fluxes between different species may take place during transport from the land to the stream and as a result of in-stream, in-river or in-lake transformations. Knowledge of the spatial and temporal variation in all nitrogen species and phosphorus fractions in a drainage basin is therefore essential if the wider environmental significance of elevated nutrient concentrations in natural waters are to be assessed. This paper reviews recent work on N and P losses from agricultural land and presents some results from two intensive agricultural catchments: Slapton, Devon and the river Windrush catchment in the Cotswolds.     

Fluctuations of Population Size and Structure in the Northern Mole Vole: Preliminary Analysis

Long-term stationary studies on the ecology of the northern mole vole (Ellobius talpinus Pall.), performed by the mark–recapture method from 1985 to 1997, have provided original data on population dynamics and structure. The analysis shows that, to reveal cyclic fluctuations of population size in this species, the period of three years should be taken as a unit of time for estimating the duration of one phase. The 12-year population cycle in E. talpinus has four distinct phases: an increase, a peak, a decline, and a minimum. At each phase, the population is characterized by certain features of family structure, age composition, birth and death rates, and the composition of migrants.     

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.     

雨水管道沉积物中典型无机氮的干期粒径分布

以南京江北新区分流制雨水管道沉积物为研究对象,考察不同粒径沉积物中铵态氮（NH₃-N）和硝态氮（NO₃^--N）干期分布特征,分析其随干期长度的变化关系,并探讨沉积物理化性质及微生物菌群结构对NH₃-N和NO₃^--N干期分布的影响.结果表明：粒径≤0.075mm的沉积物中NH₃-N占比最高（交通区30.8%,商业区36.7%）;交通区粒径≤0.075mm的沉积物中NO₃^--N占比最高（33.0%）,而商业区粒径0.075~0.15mm沉积物中NO₃^--N占比最高（34.4%）;干期长度与交通区0.075~0.15mm粒径段的沉积物中NO₃^--N含量及商业区粒径≥0.3mm的沉积物中NH₃-N含量之间的相关性均最显著;雨水管道沉积物中NH₃-N和NO₃^--N的干期分布与O-H和N-H等官能团、表面极性和亲水性、微观形貌等有一定关联;交通区雨水管道沉积物中Gemmobacter等反硝化优势菌种（相对丰度总和为20.15%）对NO₃^--N干期分布影响更显著.     

大兴安岭5种乔木树种燃烧释放PM2.5中水溶性元素特性

采用自主设计的生物质燃烧实验装置,在不同燃烧状态（明燃、阴燃）下,对大兴安岭林区5种典型乔木树种的不同部位（枝、叶、皮）燃烧释放PM_2.5中的水溶性元素特性进行研究.结果显示,不同树种间PM_2.5的排放因子差异显著,排放范围为（2.408±0.854）~（9.227±1.172）g/kg.5种乔木树种燃烧释放PM_2.5中主要检测到Mg、Ca、K等16 种元素,其中Ca、K、Zn、Mg 4种元素的排放因子明显大于其它元素.不同树种间元素排放因子差异较大,针叶树的排放因子一般高于阔叶树.除Cd元素外,不同器官间排放的元素总量无明显差异.不同树种不同器官燃烧释放PM_2.5中水溶性元素的占比顺序较为一致,其中Ca、K、Zn和Mg 4种元素的排放因子在枝、叶、皮中均较高.此外,燃烧状态对元素排放特征影响较大,Li、Mg、Ca等7种元素的排放因子均表现为明燃显著高于阴燃.     

铁基质强化新型功能性化粪池高效处理黑水的试验

以铁基质生物载体为核心,将物理、生物和化学方法结合,对化粪池进行功能强化,实现黑水的原位深度处理.探究了溶解氧（DO）和碳氮比（C/N）等因素对黑水中污染物降解的影响,并在最佳运行参数下考察了氮素在系统中的转化机制.结果表明,当C/N为7.3~8.4时,好氧生物铁基质载体池DO为2.3~2.7mg/L时,系统氨氮（NH₄⁺-N）、总氮（TN） 、COD和总磷（TP）的平均去除率分别可达90.74%、85.81%、92.65%和95.78%;当进水C/N降至3.3~4.2时,系统NH₄⁺-N、TN、COD和TP的平均去除率仍可维持在81.16%、76.62%、93.87%和94.75%.铁基质生物载体内电解作用显著强化了化粪池内TN的脱除、COD的氧化和TP的固定.氮素转化机制分析表明,内电解与反硝化菌的耦合强化了反硝化作用,降低了反硝化过程对有机碳源的需求,强化了低C/N条件下TN、TP的脱除.     

水体及沉积物氮磷水平对附植藻类的影响

为了探讨湖泊富营养化过程中沉积物及水体氮、磷浓度对附植藻类的影响,通过室内模拟实验,研究了水体及沉积物氮、磷升高对苦草(Vallisnerianatans(Lour.) Hara)上附植藻类生长、群落组成及其体内氮、磷含量的影响.结果表明,在实验条件下,随着水中氮、磷含量升高,附植藻类生物量及附植藻类氮、磷含量均呈极显著增加(p0.01).随着水体可获得的氮、磷浓度升高,附植藻类的相对丰度有所变化,舟形藻(Navicula)、小球藻(Chlorella)及微囊藻(Microcystis)相对丰度随着氮、磷水平的升高而下降,直链藻(Melosira)则相反,但舟形藻、直链藻、微囊藻、小环藻(Cyclotella)和小球藻均为群落的优势属种.沉积物氮、磷含量升高对附植藻类生物量、优势种丰度及群落氮、磷含量影响较小,均未达到显著水平(p0.05).在实验条件下,沉积物氮、磷含量对附植藻类影响不大,而水体氮、磷浓度升高显著地促进了附植藻类生长.研究结果也为解释富营养化湖泊沉水植物衰退及消亡提供了一定的科学依据.     

牛粪-化肥配施对水稻田氮磷迁移转化的影响

在控制外源N输入量相同的前提下,通过田间小区实验,探讨有机肥与化肥不同施用量（牛粪施用量：5,10,20t/hm²）对稻田田间土-水界面氮磷迁移转化特征的影响.结果表明：控制稻田水中NH₄⁺-N、NO₃^--N、TN和TP输出的最佳时期分别为施肥后的第5,30,7,20d,且TN和TP浓度随时间变化符合单指数衰减方程（0.7444≤R²≤0.9724;1.1×10^-6≤F≤0.0055）.采用牛粪部分代替化肥的施肥方式,在一定范围内能降低稻田退水中TN、TP输出负荷（41.8%、36.0%、64.3%;20.3%、39.1%、48.9%）,还可以降低稻田水中N/P,降低水体富营养化风险.同时,牛粪的施用可提高土壤中脲酶和磷酸酶的含量,促进氮磷向植物可吸收形态转化.综合经济成本和生态效益核算,采用10t有机肥代替无机肥的处理是相对经济环保的施肥方法,该施肥方式下,氮磷年输出负荷分别为17.70,1.26kg/hm².     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.