Ionic imbalance due to hydrogen carbonate from Asian dust

In an attempt to identify the ionic imbalance, hydrogen carbonate, HCO₃⁻ (bicarbonate), was determined together with the commonly determined nine major ions because the imbalance was frequently encountered in the chemical analysis of samples with high pHs. Titration method was applied for the determination of the samples with pH higher than 5.6. These samples amounted to 102 of the 1536 samples collected in Hokkaido, Northern Japan, from April 1998 to December 2002. Of the 102 samples, 74 (Group A) showed an acceptable ion balance without including HCO₃⁻. In the other 28 (Group B), however, inclusion of HCO₃⁻ successfully improved the ion balance. These results suggested that hydrogen carbonate was a potential candidate for explaining the imbalance. The hydrogen carbonate concentrations showed a strong correlation with the corresponding non-sea salt calcium (nss-Ca²⁺) concentrations, which implied that hydrogen carbonate was derived from calcium carbonate particles incorporated into falling raindrops or cloud droplets. For Group A, the relationship between hydrogen carbonate and the nss-calcium ion concentration was very similar to that for commonly suspended particles in Hokkaido. On the other hand, Group B exhibited a stronger but significantly different correlation. To the samples of Group B, a back-trajectory analysis was applied to demonstrate that the samples were associated with aerosol travelling from Northern China or Mongolia, which indicated that alkaline aerosol affected the chemistry. In consideration of these findings, the current standard of the ion balance should be critically reviewed for modification in regard to the contribution of hydrogen carbonate not only in source areas but also in receptor areas of alkaline aerosol.     

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     

基于OMI数据的东南沿海大气臭氧浓度时空分布特征研究

基于臭氧监测仪(OMI)卫星反演数据,对2005—2018年东南沿海5省区域大气臭氧柱浓度数据进行提取及分析,探讨其时空分布格局及影响因素.结果表明:①在时间变化上,14年间,该区域大气臭氧柱浓度整体呈先上升后下降的趋势,2005—2013年臭氧柱浓度持续升高,最高值为324.52 DU,高值区不断向南部区域扩大;2013—2018年臭氧柱浓度呈下降趋势,最低值为228.27 DU,但在2017、2018年略有上升.②在空间分布上,臭氧柱浓度自北向南逐渐降低,高值区集中分布在江苏及浙江省北部;低值区集中于福建省南部及广东省大部分地区.③在季节变化上,大体呈现出春夏季高于秋冬季,高值区在春夏季交替出现,秋季略高于冬季,但差异不明显.④稳定性分析表明:研究区臭氧柱浓度整体呈现中部分散、南北部集聚、差异较显著的分布格局.⑤自然因素中,风向、气温均呈现显著正相关,江淮地区的梅雨季节(降水)及华南地区的台风和暴雨也起到显著作用.⑥人文因素中,臭氧柱浓度与地区生产总值、各产业生产总值及机动车保有量均表现出正相关,其中,臭氧柱浓度与第二产业的相关度最高.另外,臭氧柱浓度与NO_x排放量表现出显著相关性.VOC_s对臭氧柱浓度的影响中,工业源是主控因素,交通源和居民源次之,电厂源对臭氧柱浓度的影响最弱.这进一步说明臭氧浓度的变化受到了诸多因素的综合影响,但气温、NO_x及VOC_s的排放是臭氧浓度变化的主导因素.     

基于卫星遥感气溶胶产品评估体系的气溶胶时空分布及变化趋势表征

卫星遥感气溶胶虽然空间覆盖度高,但不同产品数据在准确性和适用性上存在显著差异.为了能够科学定量的衡量各个产品的优劣,选择最合适的气溶胶产品,该研究以数学统计为基础,提出了一种卫星遥感气溶胶产品评估体系,并依此确定了全球6个人口密集地区的最优卫星数据集,在此基础上开展了近10年的（2009—2018）气溶胶时空变化分析.结果表明暗像元算法在植被覆盖度高的地区表现最好,深蓝算法在亮地表的沙漠干旱地区更占优势,而暗像元-深蓝融合算法在土地类型复杂且气溶胶来源多变时得以突出.人口密集的6个区域中,除了区域A和区域B,其它地区的大气污染水平整体偏高.其中,A、B、E的气溶胶负荷呈下降变化,区域F基本不变,其余区域上升变化.评估体系的建立为气溶胶遥感产品的选取提供了新的衡量方法,且该文关于气溶胶时空分布及变化的分析可以为区域性气溶胶研究提供一定的参考价值.     

深圳市秋季大气臭氧立体分布特征

利用大气O₃探测激光雷达在深圳市东部生态区和西部城区同步开展垂直观测,探究了2018年深圳市O₃立体分布在秋季光化学污染活跃期至冬季非活跃期的演变过程.结果表明：光化学反应活跃的10月,东部地面O₃浓度相对于西部地面高出约128%;地面向上至450m,O₃浓度在东部生态区发生快速降低,而在西部城区由于存在“滴定效应”,O₃浓度随高度升高而升高;450m~2km,东、西部O₃浓度均随高度升高而降低,西部城区O₃浓度水平超过东部生态区约30%;2km以上高空,东、西部O₃浓度趋同（70μg/m³）,并保持稳定,为具深圳市秋季O₃污染过程提供了较高的大气背景浓度.高污染期间,深圳市大气边界层内O₃浓度变化较为一致,西部高空的O₃区域传输作用更加显著.秋季至冬季光化学反应逐渐减弱,深圳市O₃浓度的水平和垂直空间差异逐渐减小,冬季的深圳市O₃污染基本受大气背景控制.     

近14年西北地区甲醛柱浓度的时空变化研究

基于臭氧监测仪（OMI）卫星反演数据,对2005~2018年西北4省区域大气甲醛柱浓度数据进行提取及分析,探讨其时空变化特征及影响因素.结果表明：在时间变化上,14a甲醛柱浓度整体呈先上升后下降的波动变化趋势,夏秋季显著高于冬春季,且冬季均值略高于春季.在空间分布上,甲醛柱浓度自西向东、自北向南逐渐升高,高值区集中于陕西和甘肃东南部及青海西南部;低值区集中于宁夏、青海和甘肃的西北部;稳定性呈现出东部分散、西部集聚、差异显著的分布格局.影响甲醛柱浓度变化的因素包括自然和人为因素,自然因素中,甲醛柱浓度受地形影响显著,与风向、气温均呈现显著正相关;人为因素中,甲醛柱浓度与人口密度、地区生产总值、工业废气排放量及建筑房屋竣工面积均表现出正相关关系,与工业废气排放量的相关度最高.大气中甲醛分子与气溶胶粒子二者间呈显著正相关关系,这进一步说明甲醛浓度受到了诸多因素的综合影响,但气溶胶粒子、气温及工业废气的排放是主导因素.     

NOx存在条件下相对湿度对甲苯氧化产物的影响

利用流动管反应器模拟甲苯与氧化剂×OH在NO_x存在条件下的反应,定量测定了不同相对湿度条件下（17.5%、35%、50%、70%）反应生成的部分气相产物和颗粒相产物,计算了不同相中产物产率,测量了不同相对湿度下的颗粒相有机碳（OC）产率,推导了相对湿度对甲苯氧化反应的影响机制.结果表明,相对湿度不仅对甲苯与×OH反应途径比例有影响,还对产物的产率及氧化程度有影响.     

北京地区基于化学组分的夏季气溶胶吸湿特性

利用吸湿增长光散射测量系统、黑碳仪和气相色谱质谱联用仪等仪器,于2019年7月15日~8月4日在北京地区开展了为期21d的大气气溶胶观测实验.观测期间北京市区于7月27日出现短暂的轻度污染,并在7月29日出现强降水天气.结果显示：北京市区夏季大气污染变化剧烈且短暂,大气气溶胶散射吸湿增长因子f（RH）呈现平滑连续的特点,并且降水会对f（RH）造成显著影响.7月27日PM_2.5的平均质量浓度为（92.54±47.05）μg/m³;,表现出较为剧烈的污染变化.7月28~30日平均散射吸湿增长因子f（80%±1%）分别为（1.50±0.35）,（1.43±0.36）和（1.48±0.25）,反映了降水对于大气气溶胶的湿清除作用.最后利用实验数据估算粒径吸湿增长因子gf（RH）,并建模研究f（RH）和gf（RH）的关系,模型精度R²最高可达0.698.     

东南亚生物质燃烧输送影响我国西南气溶胶辐射特性研究

利用2013年地基CE-318太阳光度计观测数据,结合中分辨率成像光谱仪（MODIS）遥感产品和HYSPLIT后向轨迹分析,研究了一次东南亚生物质燃烧污染长距离输送至中国西南昆明站点过程（2013年4月5~8日）中,气溶胶光学特性和辐射特性的变化及其可能来源.结果表明,此次污染过程期间,我国西南地区生物质燃烧活动较少,而中南半岛地区生物质燃烧活动显著.4月5~7日,昆明站点气溶胶光学厚度（AOD）升高,消光波长指数（EAE）和吸收波长指数（AAE）均增大.此外,依据EAE和AAE分类方法,5~6日昆明站点以城市工业气溶胶为主,7~8日以生物质燃烧气溶胶为主,其细模态峰值半径（0.11μm）小于5~6日（0.15μm）,7日细模态粒子体积浓度峰值（约为0.16μm³/μm²）是5日的2倍.气溶胶直接辐射强迫（ARF）日变化结果表明4月7日气溶胶对地表的降温效应达到最大,对大气的加热作用最强.气溶胶直接辐射强迫效率值（ARFE）的变化表明生物质燃烧气溶胶对大气顶的降温作用减弱.MODIS遥感以及HYSPLIT模式后向轨迹表明,此次昆明站点生物质燃烧气溶胶主要来源于东南亚地区（主要是印度北部、印缅北部和不丹地区）.     

基于RF-LSTM的鸡舍恶臭气体预测研究

以鸡舍氨气为研究对象,对鸡舍氨气预测模型进行了研究.首先,利用随机森林算法（RF）对影响鸡舍氨气浓度的环境变量进行重要性排序,选取温度、湿度、光照、气象温度、降雨量作为模型的输入变量;在此基础上,构建了基于长短时记忆神经网络（LSTM）的鸡舍氨气浓度预测模型,并将提出的预测模型应用于江苏省宜兴市某养鸡场的氨气浓度预测中,并与LSTM模型、RF-Elman模型和RF-BP模型进行了对比实验,结果表明,基于RF-LSTM模型的预测效果最好,其平均绝对误差（MAE）、平均绝对百分误差（MAPE）和均方根误差（RMSE）分别为0.9183、4.9637%和1.4262;同时,为了验证该模型的性能,本文还实现了不同时间尺度的鸡舍氨气浓度预测,提前2h、3h、4h、5h氨气预测的平均绝对误差（MAE）分别为1.6218、2.1991、2.8553和3.0677.本文提出的预测模型提高了鸡舍氨气浓度的预测精度,可为减少鸡舍恶臭气体排放提供科学依据.