Characteristics of atmospheric volatile organic compounds in urban area of Beijing: Variations, photochemical reactivity and source apportionment

Atmospheric volatile organic compounds (VOCs) were observed by an on-line gas chromatography-flame ionization detector monitoring system from November 2016 to August 2017 in Beijing. The average concentrations were winter (40.27 ± 25.25 μg/m³) > autumn (34.25 ± 19.90 µg/m³) > summer (32.53 ± 17.39 µg/m³) > spring (24.72 ± 17.22 µg/m³). Although benzene (15.70%), propane (11.02%), ethane (9.32%) and n-butane (6.77%) were the most abundant species, ethylene (14.07%) and propene (11.20%) were the key reactive species to ozone formation potential (OFP), and benzene, toluene, ethylbenzene, m-xylene + p-xylene and o-xylene (54.13%) were the most reactive species to secondary organic aerosol formation potential (SOAFP). The diurnal and seasonal variations indicated that diesel vehicle emission during early morning, gasoline vehicle emission at the traffic rush hours and coal burning during the heating period might be important sources. Five major sources were further identified by positive matrix factorization (PMF). The vehicle exhaust (gasoline exhaust and diesel exhaust) was found to be contributed most to atmospheric VOCs, with 43.59%, 41.91%, 50.45% and 43.91%, respectively in spring, summer, autumn and winter; while solvent usage contributed least, with 11.10%, 7.13%, 14.00% and 19.87%, respectively. Biogenic emission sources (13.11%) were only identified in summer. However, both vehicle exhaust and solvent usage were identified to be the key sources considering contributions to the OFP and SOAFP. Besides, the contributions of combustion during heating period and gasoline evaporation source during warm seasons to OFP and SOAFP should not be overlooked.     

长江流域总磷污染:分布特征·来源解析·控制对策

针对长江流域总磷污染,开展总磷污染时空特征分析,选择长江流域总磷污染最严重的上游地区岷江和沱江为典型区,分析总磷来源,提出总磷污染控制对策.研究表明:2016年开始总磷成为长江流域主要污染因子,其中上游污染最重,中游污染最轻,总体呈降低趋势;长江流域枯/平水期总磷污染较重,丰水期污染较轻,说明流域主要污染负荷来自点源.总体来说,造成长江流域总磷较高的原因有:磷矿开采和磷化工的污染源高负荷排放,造成部分河段水质严重超标;基础设施建设滞后,城镇生活污染源排放影响河流水质;畜禽养殖废物资源化利用不足;生态流量不足,加剧水污染问题;水污染治理导向不全面和污染源监管措施不系统,影响总磷水质同步改善.针对长江流域总磷污染特征,按照"分区控制、分类治理""突出重点、精准施策"原则,提出长江流域总磷污染控制建议:①抓住长江流域上游重点片区,开展流域总磷污染整治. ②抓住磷化工、城镇生活和畜禽养殖等三类涉磷重点污染源的治理,控制磷污染负荷排放. ③抓住环境监管有效手段,进一步完善水环境标准和监管体系.      

中国臭氧污染的空间分布和健康效应

基于2017年全国1365个监测站点的实时监测数据，运用空间数据统计模型揭示近地面臭氧（O₃）污染的时空分布格局，并利用BenMap工具在10km×10km空间网格尺度上估计O₃污染的健康损失和健康经济价值.结果表明，O₃浓度具有较强的季节性变化，呈倒"V"型变化趋势，在空间分布上呈现明显的集聚性，即高值或低值区域集中分布，具有较强的空间正相关性；通过O₃暴露系数模拟人群室内、室外O₃暴露情况，在统计意义上估计得到2017年O₃污染共计造成我国全因早逝人数98473例（95%置信区间：53419~143292），其中心血管疾病早逝风险约占45%，以不同学者估算得到的单位统计生命价值为基础，估计得到的健康经济损失在197~978亿元之间，约占2017年全国GDP的0.05%~0.26%.     

城市污水A~2/O处理系统好氧池N_2O和NO的释放特征及影响因素

以某污水处理厂好氧曝气池为研究对象,利用N_2O和NO在线检测仪,监测好氧池不同空间位置处N_2O和NO的释放量,考察了氮素浓度、溶解氧及p H值对N_2O和NO释放量的影响.该污水处理厂好氧池DO浓度在0.24~1.12mg/L之间,且大多处于0.6mg/L左右,较低的DO导致NO2--N浓度沿水流推流方向不断积累;相应的,N_2O和NO释放量沿水流推流方向不断升高,并于好氧池末段达到最高值.NO释放NO2--N量与浓度显著正相关,N_2O释放量与NO2--N浓度也有一定的相关性,但没有NO显著.基于空间检测数据估算获得该污水处理厂N_2O释放量占进水NH_4+-N比例为6.34%~8.83%,NO释放量占进水NH4+-N比例为0.033%~0.034%.     

辽河流域典型支流土地利用方式对大型底栖动物群落时空分布特征的影响

通过辽河流域典型支流(清河和凡河)不同土地利用类型区3个水期大型底栖动物及环境因子调查,研究土地利用方式对大型底栖动物分布特征的影响.结果表明,河流大型底栖动物群落四节蜉科(Baetidae)、扁蜉科(Heptageniidae)、纹石蛾科(Hydropsychidae)及石蝇科(Perlidae)等生物类群主要分布在辽河流域内林地为主的河流中;以耕地和居民点为主的土地利用方式下,河流大型底栖动物主要以摇蚊科(Chironomidae)类群为主;颤蚓科(Tubificidae)等寡毛类大型底栖动物在以城市发展为主的土地利用区域内河流中优势明显.河流中大型底栖动物生物多样性、丰富度及EPT%由高到低依次为林地耕地居民点城市,FBI值由低到高依次为林地耕地居民点城市.不同土地利用方式导致河流生境因子空间差异性显著,大型底栖动物群落相似性较低,且生物密度在空间上呈显著差异,但时间上差异不显著.底质、DO与大型底栖动物多样性等指数呈正相关关系,TN、TP、NH3-N、BOD5、CODCr与大型底栖动物生物指数呈不同程度负相关关系.生物与环境联合分析(BIO-ENV)表明,底质、DO、TN、BOD5是影响区域内大型底栖动物群落特征的最显著的主导环境因子.综合上述研究结果可得,土地利用方式通过对河流生境及水质产生影响,进而使大型底栖动物群落组成及多样性特征发生明显变化.     

Comparison of atmospheric concentrations of currently used pesticides between urban and rural areas during intensive application period in Alsace (France) by using XAD-2® based passive samplers

XAD-2® passive samplers (PAS) have been exposed simultaneously for 14 days on two sites, one rural and one urban, situated in Alsace (East of France) during intensive pesticides application in agriculture (between March and September). PAS have been extracted and analyzed for current-used pesticides and lindane with an analytical method coupling accelerated solvent extraction (ASE), solid-phase microextraction (SPME) and GC/MS/MS. Results show the detection of pesticides is linked to the period of application and spatial and temporal variabilities can be observed with these PAS during the selected sampling period. The spatial and temporal variability is comparable to the one previously observed by comparing data obtained with PAS with data from Hi.-Vol. samplers in an urban area. Sampling rates were calculated for some pesticides and values are comparable to the data already available in the literature. From these sampling rates, concentrations in ng m^?3 of pesticides in PAS have been calculated and are in the same order of magnitude as those obtained with Hi.Vol. sampling during the same period of time.     

基于时空混合效应模型的京津冀PM2.5浓度变化模拟

为揭示京津冀地区高精度PM_2.5的时空分布特征,以空间分辨率为1 km的MAIAC AOD数据为主要预测因子,以气象数据、植被指数、夜间灯光数、人口密度和海拔数据作为辅助因子,构建了一种新的时空混合效应模型（STLME）,在拟合最优次区域划分方案基础上对京津冀地区PM_2.5浓度进行预测分析.结果表明,基于STLME模型的ρ(PM_2.5)预测精度高于传统的线性混合效应模型（LME）,其十折交叉验证（CV）R²为0.91,明显高于LME模型的0.87,说明STLME模型在同时校正PM_2.5-AOD关系的时空异质性方面具有优势.最优次区域划分方案识别出PM_2.5-AOD关系的空间差异,并结合缓冲区平滑方法,提高了STLME模型预测精度.京津冀PM_2.5浓度时空变化差异显著,高值区主要分布在以石家庄、邢台和邯郸为中心的河北南部,低值区则位于燕山-太行山区;冬季PM_2.5污染最严重,其次是秋季和春季,夏季污染最轻.STLM...     

Spatiotemporal Features of Severe Air Pollution in Northern Taiwan (8 pp)

Background, Aims and Scope This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau. Methods Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events. Results and Discussion Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan. Conclusion According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms-1, 360 m and 800 m2s-1, respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed. Recommendation and Outlook By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.     

Individual variation in pheromone emission and termination patterns in female Anomala cuprea

Summary. Individual variations in pheromone emission patterns were examined in a scarab beetle, Anomala cuprea Hope (Coleoptera: Scarabaeidae), by headspace collection of airborne volatiles from individual females. The amount of pheromone obtained varied among virgin females, and about 16% of these females (“silent” females) did not emit detectable amount of pheromone throughout the experimental period. There was no clear temporal pattern of peak pheromone emission for 19 days after the onset. More than half of the laboratory mated females completely stopped releasing pheromone after the first mating, while the rest of them continued releasing pheromone, frequently followed by additional mating. Received 26 March 2001; accepted 28 January 2002.