成都市夏季大气挥发性有机物污染及其对二次有机气溶胶生成的贡献

于2014年7月8日至8月13日在成都市城区和工业区选取两个点位开展挥发性有机物(VOCs)样品采集工作,分析结果显示,成都市夏季城区大气中VOCs质量浓度在34.1~458.8μg/m3,平均值为(137.3±91.8)μg/m3;工业区大气中VOCs质量浓度在26.7~474.9μg/m3,平均值为(135.9±103.5)μg/m3。早高峰时段(7:00~10:00)两个点位VOCs的浓度水平均高于其他时段,说明VOCs浓度受机动车排放的影响较为明显。用·OH消耗速率和臭氧生成潜势评估VOCs大气化学反应活性,结果显示,芳香烃和烯烃是影响大气化学反应活性的关键组分。城区和工业区的二次有机气溶胶(SOA)的生成潜势分别为4.859、4.559μg/m3,芳香烃不仅是臭氧生成潜势的关键活性组分,同时也是SOA的重要前体物。     

清潩河许昌段表层沉积物污染评价

为了解水质污染对沉积物质量的影响,在清潩河许昌段河道内采集10个代表性点位处的沉积物样品,对pH、有机质、总氮、总磷、Cu、Zn、As、Cr和Pb含量进行分析,根据环境背景值或生态风险阈值对各指标及各点位的污染状况进行评价,与文献中报道的其他流域沉积物中污染物含量进行了对比,并对清潩河河道沉积物的污染原因进行分析。结果表明,清潩河河道表层沉积物整体处于污染状态,部分河道为重污染;与其他流域相比,清潩河河道表层沉积物中有机质、总氮和总磷的污染程度比较严重,尤其总氮的污染尤为突出。水资源匮乏、造纸厂及城镇污水处理厂的排污是造成上述结果的主要原因。     

连云港市大气污染物时间序列的长程相关性分析

运用R/S分析(Rescaled Range Analysis)方法对连云港市3个大气环境定位监测点近10年来的SO2、NOx和TSP序列数据进行了时间序列的长程相关性分析.结果表明,连云港市SO2、NOx和TSP月均值序列的Hurst指数在0.500～1.000,表现出明显的长程相关性,并且这种相关性程度的强弱随着城市功能区的不同而表现出一定的差异.研究结果对于认识连云港市城市大气环境质量变化过程和科学制定环保决策具有重要意义.     

西安南郊采暖期大气颗粒物PM2.5的污染特征分析

为研究西安市南郊地区采暖期大气颗粒物PM2.5的污染浓度及水溶性成分,使用颗粒物采样器于2009年1月6日～2009年2月15日进行PM2.5采样.将24 h分为8个阶段,每天3 h定时采样.结果表明,西安市南郊地区采暖期PM2.5明显污染,24 h中PM2.5污染状况最严重的时段为21:00～23:59;PM2.5中NH+4、NO-3和SO2-4是其最主要的水溶性组分,在PM2.5中的平均质量混合比分别为10.225%、13.698%和15.650%,三者在PM2.5中质量混合比最高的时段分别为06:00～08:59、03:00～05:59和18:00～20:59.     

西安采暖期PM2.5及其水溶性无机离子的时段分布特征

为了探讨西安市采暖期大气颗粒物PM2.5及其水溶性无机成分的污染水平,于2010年1月4日—2月1日按一天8个时段(每个时段3 h)连续采集PM2.5样品四周,每周更换一次滤膜。结果显示,西安市采暖期PM2.5的质量浓度时段差异较大,呈现明显的双峰分布特征:21:00—24:00时段(147.516μg/m3)和09:00—12:00时段(141.678μg/m3)。4种被测水溶性无机组分总浓度为39.801μg/m3,占PM2.5总浓度的30.5%。SO24-和NO3-是最主要组分,占到4种无机组分的86.2%。各离子间相关分析显示,Cl-只与NO3-有较强的相关性,表明机动车尾气对Cl-有较大的贡献。SO24-和NO3-时段分布规律较为相似,与PM2.5浓度的时段分布特征相反:在PM2.5污染最轻的15:00—18:00时段,SO24-和NO3-的相对含量达到一天中的最高浓度时段,而在PM2.5双峰时段,它们的含量有所降低。     

西安南郊采暖期大气颗粒物PM_（2.5）的污染特征分析

为研究西安市南郊地区采暖期大气颗粒物PM2.5的污染浓度及水溶性成分,使用颗粒物采样器于2009年1月6日-2009年2月15日进行PM2.5采样。将24 h分为8个阶段,每天3 h定时采样。结果表明,西安市南郊地区采暖期PM2.5明显污染,24 h中PM2.5污染状况最严重的时段为21：00-23：59;PM2.5中NH4^＋、NO3^-和SO42^-是其最主要的水溶性组分,在PM2.5中的平均质量混合比分别为10.225%、13.698%和15.650%,三者在PM2.5中质量混合比最高的时段分别为06：00-08：59、03：00-05：59和18：00-20：59。     

内梅罗污染指数在运河水质评价中的应用

应用内梅罗污染指数公式,计算了１９９４－１９９８年运河（杭州段）各监测点位的污染指数,用较直观的数据来评价运河（杭州段）的污染状况。     

南昌市电磁辐射环境现状与评价

以南昌市东湖区为研究区,用PMM8053B电磁辐射分析仪对研究区内155个监测点的电磁辐射环境进行监测;应用统计学和风险概率计算法对研究区电磁辐射环境进行分析和评价,探讨该区域电磁辐射分布规律、来源及污染水平,并在此基础上探讨电磁污染风险概率。结果表明:研究区射频电场强度及功率密度、工频电场强度及磁感应强度均低于《电磁环境控制限值》(GB8702—2014)规定的限值,电磁辐射环境污染风险概率几乎为0;昼、夜射频电场强度与基站使用程度有关,昼、夜工频电场强度和磁感应强度主要与变电站及高压线布设相关;由于公众活动高峰时段不一致,导致同一点位昼、夜电磁辐射水平也有差异;若电磁辐射以年均8%的速度递增,研究区电磁辐射水平将在57.07a后超过GB 8702—2014的限值。     

加速溶剂萃取-GC/MS法研究济南市环境空气颗粒物中的多环芳烃

以济南市为研究区域,利用TH-150C型智能中流量总悬浮颗粒物采样器采集了7个点位环境空气颗粒物样品,采用加速溶剂萃取、旋转蒸发、硅胶柱层析、氮吹仪氮吹后用GC/MS测定的实验研究方法,获得了济南市7个点位环境空气颗粒物中16种PAHs的质量浓度和ΣPAHs值.结果表明,7个点位环境空气颗粒物中ΣPAHs的含量在103...     

基于主成分和粒子群优化支持向量机的水质评价模型

水质的评价是治理水污染必不可少的工作。为了准确、快速地对水质进行评价,利用主成分分析法从水质监测常见的多个物化指标提取出主成分,然后将主成分作为支持向量分类机的输入,利用历史数据进行水质评价训练并用粒子群算法优化参数,构造出水质评价模型,将从物化指标中得出的主成分代入此模型即可得到水质类别。最后,选取水质监测点实测数据进行试验,结果表明,模型的水质评价结果准确且稳定。     

Evaluation of weather conditions for methyl bromide ambient air monitoring in Monterey, Santa Cruz, and Kern Counties in the year 2000

Weather condition is one of the most important factors affecting spatial and temporal distributions of air pollutants, especially for short-term air dispersion. Abnormal weather conditions might lead to higher or lower ambient air concentrations than they would be under normal weather conditions. Therefore, testing for normality of weather conditions during the air monitoring period is an essential step for evaluating ambient air monitoring results. In this paper, a distance method was used to select a most representative weather station from the available candidates. An array of meteorological elements were identified that affect air dispersion and transportation. A statistical method was used to determine whether the weather conditions during the air monitoring period were significantly different from that of previous years. Using methyl bromide ambient air monitoring as a case study, this paper documents the methods, procedures, and results of weather analysis for Monterey, Santa Cruz, and Kern Counties during ambient air monitoring periods for methyl bromide in the year 2000. With a few exceptions, the meteorological elements and atmospheric stability factors, such as wind speeds, wind directions, and stability classes, during the monitoring period were in the normal range. Although there were higher frequencies of stable atmospheric conditions in Monterey/Santa Cruz Counties than in Kern County, weather conditions during the monitoring period were not significantly different from normal local weather conditions of previous years. Consequently, the subchronic air concentrations observed during the ambient air monitoring periods for methyl bromide in the year 2000 was taken under typical weather conditions of those areas at that time of the year.     

Corona ion induced atmospheric potential gradient perturbations near high voltage power lines

High voltage power lines are known to produce corona ions that can be carried from the line by the wind, progressively attaching to ambient aerosol particles and causing fluctuations in the Earth’s atmospheric potential gradient. A fixed site monitoring station was installed near two power lines, which recorded the potential gradient at 1 s intervals and the ambient weather conditions every 10 min. The station ran continuously from January 2007 to December 2008, accumulating long-term data. Results from 2008 show an increase in potential gradient variability when a wind is blowing across the power lines towards the station, indicating that corona ions may be present in this environment.     

The longitudinal dependence of black carbon concentration on traffic volume in an urban environment

The purpose of this study was to evaluate the effect of traffic volume on ambient black carbon (BC) concentration in an inner-city neighborhood "hot spot" while accounting for modifying effects of weather and time. Continuous monitoring was conducted for 12 months at the Baltimore Traffic Study site surrounded by major urban streets that together carry over 150,000 vehicles per day. Outdoor BC concentration was measured with an Aethalometer; vehicles were counted pneumatically on two nearby streets. Meteorological data were also obtained. Missing data were imputed and all data were normalized to a 5-min observational interval (n = 105,120). Time-series modeling accounted for autoregressively (AR) correlated errors. This study found that outdoor BC was positively correlated at a statistically significant level with neighborhood-level vehicle counts, which contributed at a rate of 66 +/- 10 (SE) ng/m3 per 100 vehicles every 5 min. Winds from the SW-S-SE quarter were associated with the greatest increases in BC (376-612 ng/m3). These winds would have entrained BC from Baltimore's densely trafficked central business district, as well as a nearby interstate highway. The strong influence of wind direction implicates atmospheric transport processes in determining BC exposure. Dew point, mixing height, wind speed, season, and workday were also statistically significant predictors. Background exposure to BC was estimated to be 905 ng/m3. The optimal, statistically significant representation of BC's autocorrelation was AR([1:6]) x 288 x 2016, where the short-term AR factor (lags 1-6) indicated that BC concentrations are correlated for up to 30 min, and the AR factors for lags 288 and 2016 indicate longer-term autocorrelations at diurnal and weekly cycles, respectively. It was concluded that local exposure to BC from mobile sources is substantially modified by meteorological and temporal conditions, including atmospheric transport processes. BC concentration also demonstrates statistically significant autocorrelation at several time scales.     

Elevated atmospheric CO2 concentration and temperature across an urban–rural transect

The heat island effect and the high use of fossil fuels in large city centers are well documented, but by how much fossil fuel consumption is elevating atmospheric CO₂ concentrations and whether elevations in both atmospheric CO₂ and air temperature from rural to urban areas are consistently different from year to year are less well known. Our aim was to record atmospheric CO₂ concentrations, air temperature and other environmental variables in an urban area and compare it to suburban and rural sites to see if urban sites are experiencing climates expected globally in the future with climate change. A transect was established from Baltimore city center (Urban site), to the outer suburbs of Baltimore (suburban site) and out to an organic farm (rural site). At each site a weather station was set-up to monitor environmental variables for 5 years. Atmospheric CO₂ was consistently and significantly increased on average by 66 ppm from the rural to the urban site over the 5 years of the study. Air temperature was also consistently and significantly higher at the urban site (14.8 °C) compared to the suburban (13.6 °C) and rural (12.7 °C) sites. Relative humidity was not different between sites whereas the vapor pressure deficit (VPD) was significantly higher at the urban site compared to the suburban and rural sites. An increase in nitrogen deposition at the rural site of 0.6% and 1.0% compared to the suburban and urban sites was small enough not to affect soil nitrogen content. Dense urban areas with large populations and high vehicular traffic have significantly different microclimates compared to outlying suburban and rural areas. The increases in atmospheric CO₂ and air temperature are similar to changes predicted in the short term with global climate change, therefore providing an environment suitable for studying future effects of climate change on terrestrial ecosystems.     

Vertical and diurnal characterization of volatile organic compounds in ambient air in urban areas

More than half of the world's population lives in cities, and their populations are rapidly increasing. Information on vertical and diurnal characterizations of volatile organic compounds (VOCs) in urban areas with heavy ambient air pollution can help further understand the impact of ambient VOCs on the local urban environment. This study characterized vertical and diurnal variations in VOCs at 2, 13, 32, 58, and 111 m during four daily time periods (7:00 to 9:00 a.m., 12:00 to 2:00 p.m., 5:00 to 7:00 p.m., and 11:00 p.m. to 1:00 a.m.) at the upwind of a high-rise building in downtown, Kaohsiung City, Taiwan. The study used gas chromatography-mass spectrometry to analyze air samples collected by silica-coated canisters. The vertical distributions of ambient VOC profiles showed that VOCs tended to decrease at greater heights. However, VOC levels were found to be higher at 13 m than at ground level at midnight from 11:00 p.m. to 1:00 a.m. and higher at 32 than 13 m between 7:00 and 9:00 a.m. These observations suggest that vertical dispersion and dilution of airborne pollutants could be jointly affected by local meteorological conditions and the proximity of pollution sources. The maximum concentration of VOCs was recorded during the morning rush hours from 7:00 to 9:00 a.m., followed by rush hours from 5:00 to 7:00 p.m., hours from 12:00 to 2:00 p.m., and hours from 11:00 p.m. to 1:00 a.m., indicating that the most VOC compounds in urban air originate from traffic and transportation emissions. The benzene-toluene-ethyl benzene-xylene (BTEX) source analysis shows that BTEX at all heights were mostly associated with vehicle transportation activities on the ground.     

Peroxyacetyl nitrate (PAN) in the urban atmosphere

Peroxyacetyl nitrate (PAN) in air has been well known as the indicator of photochemical smog due to its frequent occurrences in Seoul metropolitan area. This study was implemented to assess the distribution characteristics of atmospheric PAN in association with relevant parameters measured concurrently. During a full year period in 2011, PAN was continuously measured at hourly intervals at two monitoring sites, Gwang Jin (GJ) and Gang Seo (GS) in the megacity of Seoul, South Korea. The annual mean concentrations of PAN during the study period were 0.64 ± 0.49 and 0.57 ± 0.46 ppb, respectively. The seasonal trends of PAN generally exhibited dual peaks in both early spring and fall, regardless of sites. Their diurnal trends were fairly comparable to each other. There was a slight time lag (e.g., 1 h) in the peak occurrence pattern between O₃ and PAN, as the latter trended to peak after the maximum UV irradiance period (16:00 (GJ) and 17:00 (GS)). The concentrations of PAN generally exhibited strong correlations with particulates. The results of this study suggest that PAN concentrations were affected sensitively by atmospheric stability, the wet deposition of NO₂, wind direction, and other factors.     

Differences between Weekday and Weekend Air Pollutant Levels in Atlanta; Baltimore; Chicago; Dallas–Fort Worth; Denver; Houston; New York; Phoenix; Washington,DC; and Surrounding Areas

Abstract

We evaluated day-of-week differences in mean concentrations of ozone (O₃) precursors (nitric oxide [NO], nitrogen oxides [NO_x], carbon moNO_xide [CO], and volatile organic compounds [VOCs]) at monitoring sites in 23 states comprising seven geographic focus areas over the period 1998– 2003. Data for VOC measurements were available for six metropolitan areas in five regions. We used Wednesdays to represent weekdays and Sundays to represent weekends; we also analyzed Saturdays. At many sites, NO, NO_x, and CO mean concentrations decreased at all individual hours from 6:00 a.m. to 3:00 p.m. on Sundays compared with corresponding Wednesday means. Statistically significant (p < 0.01) weekend decreases in ambient concentrations were observed for 92% of NO_x sites, 89% of CO sites, and 23% of VOC sites. Nine-hour (6:00 a.m. to 3:00 p.m.) mean concentrations of NO, NO_x, CO, and VOCs declined by 65, 49, 28, and 19%, respectively, from Wednesdays to Sundays (median site responses). Despite the large reductions in ambient NO_x and moderate reductions in ambient CO and VOC concentrations on weekends, ozone and particulate matter (PM) nitrate did not exhibit large changes from week-days to weekends. The median differences between Wednesday and Sunday mean ozone concentrations at all monitoring sites ranged from 3% higher on Sundays for peak 8-hr concentrations determined from all monitoring days to 3.8% lower on Sundays for peak 1-hr concentrations on extreme-ozone days. Eighty-three percent of the sites did not show statistically significant differences between Wednesday and weekend mean concentrations of peak ozone. Statistically significant weekend ozone decreases occurred at 6% of the sites and significant increases occurred at 11% of the sites. Average PM nitrate concentrations were 2.6% lower on Sundays than on Wednesdays. Statistically significant Sunday PM nitrate decreases occurred at one site and significant increases occurred at seven sites.     

环境监测优化布点的投影寻踪回归分析法

应用投影寻踪回归（ＰＰＲ） 建模技术,分析环境测点的污染物监测数据之间的关系。通过建模过程中的数据生成,获得各测点的数据对环境质量总体状况的权重贡献率。根据权重贡献大小,进行环境测点的优选。用ＰＰＲ 分析法从成都市３ 项大气污染物的１２ 个环境测点中,优选出５ 个测点,其保留信息量约占全部测点信息量的９０ ％ 。     

What determines selection and abandonment of a foraging patch by wild giant pandas (Ailuropoda melanoleuca) in winter?

Background, aim, and scope  Foraging patches can be described as a nested hierarchy of aggregated resources, implying that study of foraging by wild animals should be directed across different spatial scales. However, almost all previous research on habitat selection by the giant panda has concentrated upon one scale. In this research, we carried out a field study to understand foraging patch selection by giant pandas in winter at both microhabitat and feeding site scales and, for the first time, attempted to understand how long it would stay at the feeding sites before moving on. Materials and methods  The field survey was conducted from November 2002 to March 2003 at Fengtongzhai Nature Reserve (102°48′–103°00′ E, 30°19′–30°47′ N), Baoxing County of Sichuan Province, China, to collect data in both microhabitat and control plots. The microhabitat plots were located by fresh feces or foraging traces left by giant pandas, and the control plots were established to reflect the environment. Within each microhabitat plot, one 1 × 1 m² plot was centralized at the center of each feeding site, in which numbers of old bamboos and old shoots, including eaten and uneaten, were counted, respectively. Results  The results showed that winter microhabitats selected by this species were characteristic of gentle slopes and high old-shoot proportions and that the latter was even higher at feeding sites. Two selection processes, namely, from the environment to microhabitats and from the latter to feeding sites, were found during this species’ foraging patch utilization. Giant pandas preferred to eat old shoots to old bamboo at feeding sites in winter and did not leave unless old-shoot density fell to lower than the average in the environment. Discussion  Both microhabitats and feeding sites selected by giant pandas were characteristic of high old-shoot density, indicating that the preferred food item had a significant influence upon its foraging patch selection. The preference for gentle slopes by giant pandas was presumed to save energy in movement or reflect the need to sit and free its fore-limbs to grasp bamboo culms when feeding but also seemed to be correlated with an easier access to old shoots. The utilization of old shoots at feeding sites was assumed to help maximize energy or nutrient intake during their foraging. Conclusions  The difference between microhabitat plots and control plots and between microhabitats and feeding sites uncovered a continuous selection process from the environment via microhabitats to feeding sites. The utilization of old shoots at feeding sites was parallel to the marginal value theorem. The selection and abandonment of foraging patches by giant pandas was an optimal behavioral strategy adapted to their peculiar food with high cellulose and low protein. Recommendations and perspectives  Our results uncovered the importance of multiple scales in habitat selection research. To further understand the process of habitat selection, future research should pay more attention to resolve the question of how to locate foraging patches under dense bamboo forests by the giant panda, which was traditionally considered to have poor eyesight, although our research has answered what type of habitats the giant panda prefers and when to leave.