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     

对导热炉大气污染物排放执行标准的探讨

目前，相关环境标准中均未明确制定关于导热炉大气污染物的排放内容。因此，对于导热炉的大气污染排放，各环境管理及监测部门均没有统一的执行标准。本文从导热炉工作原理、污染物排放特点等方面进行了分析，对其执行标准提出了个人的观点。     

南宁城市大气污染对人体健康的危害及治理对策

南宁市大气属煤烟型污染 ,大气的主要污染物为SO2 、NOx、TSP、降尘 ,虽然随着环境管理和污染治理工作的加强 ,污染物浓度逐年下降 ,但是工业区大气污染仍然较重。污染物流行病学调查显示 :工业区癌症和呼吸系统疾病死亡率均高于全市平均水平 2倍左右 ,城区又高于郊区 2倍 ,大气污染综合指数与呼吸内科门诊就诊人数呈正相关。用邓聚龙的灰色系统理论分析得知污染物对癌症和呼吸系统疾病死亡率有关联 ,关联度从大到小排序为 :TSP >降尘 >SO2 >NOx ,最后提出了大气污染治理的对策。     

Effect of Mountain Relief and Industrial Air Pollution on Biometric Characteristics of Pine Stands

The effect of mountain relief and industrial air pollution on biometric parameters of pine stands was studied. The empirical–statistical models of the dependence of biometric characteristics on the parameters of forest sites were developed using raster modeling and multivariate analysis. The possibility of predicting changes in the biometric parameters at any site on the basis of these models is shown.     

安全评价方法在危险废物处置环境风险评价中的应用探讨

对安全评价方法在危险废物处置建设项目环境风险评价中的运用进行初步探索。主要用"工艺过程风险因素分析表"对工艺过程潜在风险性识别;用蒙德法进行源项分析;用池火灾模型、蒸气云爆炸伤害模型对易燃、易爆物质的火灾、爆炸等重大事故后果进行计算,得出人员的伤亡半径和财产损失半径等参数,以便于判断风险的可接受水平。分析结果表明:采用安全评价方法对危险废物处置建设项目进行环境风险评价是适用的、可行的。     

污泥加热预处理对中温厌氧消化的影响

对污泥加热预处理给中温厌氧混合消化和污泥单独消化带来的影响进行了研究.研究结果表明,污泥加热预处理有利于提高混合消化对 COD 的去除率,尤其是 SCOD 的去除率由 77%增长到 93%,但不利于 TS 和 VS 的去除;而对污泥单独消化,预处理则不利于有机物的去除.采用加热预处理后的污泥进样,混合消化和污泥单独消化的甲烷产气量均有所提高.     

重量法测定含动、植物油和矿物油废水应注意的问题

废水监测中，用重量法测定污（废）水中的动、植物油和矿物油是常用方法之一。为了保证测试结果的准确性，减少误差，提高分析测试质量，作者以多年的实践经验结合塔里木油田采油厂生产污水和餐饮行业生活废水的测定，对测定两种不同含油类废水应注意的问题进行探讨。供同行在实际工作中参考。     

基于不同废污泥源的短程反硝化快速启动及稳定性

为探究不同废污泥源快速启动短程反硝化和实现稳定NO_2~--N积累的可行性,在3个完全相同的SBR反应器(S1、S2和S3)分别接种:实验室城市污水反硝化除磷系统排泥、城市污水厂剩余污泥及河涌底泥,比较其短程反硝化启动快慢和NO_2~--N积累特性,考察系统短程反硝化活性和NO_3~--N→NO_2~--N转化性能,并从微生物学角度分析反应器功能菌群特征.结果表明,在乙酸钠为唯一碳源、高碱度和适宜COD/NO_3~--N比进水条件下,3个SBR短程反硝化反应器在短时间内均能够成功启动,系统平均NO_3~--N→NO_2~--N转化率为S1 S2 S3(75. 92% 73. 36% 69. 90%).同时发现持续低温条件下S1和S2呈现不同程度的短程反硝化性能恶化趋势,但S3能够稳定维持良好NO_2~--N积累性能.微生物高通量测序表明,变形菌门和拟杆菌门居PD系统主导地位,3个短程反硝化反应器NO_2~--N积累关键功能菌属Thauera属丰度差异明显:S3 S1 S2(25. 09% 4. 71% 3. 60%),表明S3具备稳定高效的NO_2~--N积累性能,同时高丰度Thauera属可能是维持低温短程反硝化活性的重要原因.     

京津冀大气污染的时空分布与人口暴露

经济的快速发展和城市化导致京津冀地区的空气质量不断恶化,已经引起学术界广泛的关注.为了揭示近年来京津冀地区大气污染状况,本研究基于中国空气质量在线监测分析平台发布的PM_(2.5)、PM_(10)、SO_2、CO、NO_2和O_3_8 h_max长期监测数据,采用统计学的方法分析了2014—2018年京津冀13个市这6种污染物的时空变化特征,结合各城市人口数据,评估了在此背景下该地区PM_(2.5)和O_3_8 h_max的人口暴露风险.结果表明:京津冀地区PM_(2.5)、PM_(10)、SO_2、CO和NO_2近年来整体上呈下降趋势,而O_3_8 h_max则呈上升趋势.总体而言,PM_(2.5)、PM_(10)、SO_2、CO和NO_2表现为冬季最高、春秋季次之、夏季最低的特征,而O_3_8 h_max则表现为夏季春季秋季冬季的特点,并在月变化上呈倒"V"型,从1月份开始逐渐上升,在6月份达到峰值,而后又逐渐下降.空间上,PM_(2.5)、PM_(10)、SO_2、CO和NO_2呈现南高北低的分布特征,而O_3_8 h_max在2014—2016年呈现北高南低的分布特征,但在2017—2018年则呈现南高北低的分布特点.此外,京津冀北部地区PM_(2.5)的来源主要是一次气溶胶,而二次气溶胶是中部地区PM_(2.5)的主要来源.除秦皇岛、承德和张家口外,其他城市细粒子在颗粒物中占的比重较大.随着近年来PM_(2.5)浓度的降低,暴露于高浓度的PM_(2.5)中的人口比例逐年减少,但距离年平均浓度限值还相差很远.除2014年外,暴露在O_3浓度超标情况下的人口在2015—2017年逐渐上升.     

哈尔滨市秸秆焚烧大气污染负荷估算方法研究

本文针对近些年哈尔滨秋末冬初大气污染程度增加的溯源问题,基于静风污染气象及降雪对秸秆焚烧的影响等基本假设,采用箱式模型和优化拟合的方法对秸秆焚烧产生污染物的源强及其负荷进行了估算.通过对2015年和2017年典型时段数据的优化拟合得到降雪前重污染天气下PM₁₀排放源强分别为20.16、21.83 μg·m^-2·s^-1,CO的排放源强分别为149.32、138.65 mg·m^-2·s^-1;降雪后重污染天气下PM₁₀排放源强分别为15.98、7.09 μg·m^-2·s^-1,CO的排放源强分别为122.91、89.21 mg·m^-2·s^-1.由降雪前后各污染物的源强差得到2015年和2017年秸秆焚烧产生的PM₁₀的排放源强分别为4.18、14.74 μg·m^-2·s^-1,负荷分别为20.73%、67.52%;CO的排放源强分别为26.41、49.44 mg·m^-2·s^-1,负荷分别为17.69%、35.66%.本文为相关清单的研究提供了一种客观的校核方法,具有重要的社会、环境及现实意义.