A multidecadal oscillation in precipitation and temperature series is pronounced in low flow series from Puget Sound streams

In Pacific Northwest streams, summer low flows limit water available to competing instream (salmon) and out-of-stream (human) uses, creating broad interest in how and why low flows are trending. Analyses that assumed linear (monotonic) change over the last ~60 years revealed declining low flow trends in minimally disturbed streams. Here, polynomials were used to model flow trends between 1929 and 2015. A multidecadal oscillation was observed in flows, which increased initially from the 1930s until the 1950s, declined until the 1990s, and then increased again. A similar oscillation was detected in precipitation series, and opposing oscillations in surface temperature, Pacific Decadal Oscillation, and Interdecadal Pacific Oscillation series. Multidecadal oscillations with similar periods to those described here are well known in climate indices. Fitted model terms were consistent with flow trends being influenced by at least two drivers, one oscillating and the other monotonic. Anthropogenic warming is a candidate driver for the monotonic decline, and variation in (internal) climatic circulation for the oscillating trend, but others were not ruled out. The recent upturn in streamflows suggests that anthropogenic warming has not been the dominant factor driving streamflow trends, at least until 2015. Climate projections based on simulations that omit drivers of multidecadal variation are likely to underestimate the range, and rate of change, of future climatic variation.     

2016—2017年武汉市城区大气PM2.5污染特征及来源解析

利用2016年1月至2017年9月湖北省环境监测中心站大气复合污染自动监测站的在线监测数据，对武汉市城区PM_2.5的污染特征及主要来源进行解析。结果表明，武汉市城区PM_2.5质量浓度呈现出明显的季节差异，季节变化规律为冬季>春季>秋季>夏季。水溶性离子的主要成分SO₄^2-、NO₃^-和NH₄⁺占总离子质量浓度的82.0%。PM_2.5中阴离子相对阳离子较为亏损，颗粒整体呈碱性。夏季气态污染物的氧化程度较高且SO₂较NO₂氧化程度高。后向轨迹分析结果表明，区域传输是武汉市PM_2.5的一个重要来源，在4个典型重污染阶段，武汉市分别受到局地、东北、西北及西南方向气团传输的影响。PMF模型解析出武汉市PM_2.5五大主要来源及平均贡献率：扬尘22.0%、机动车排放27.7%、二次气溶胶21.6%、重油燃烧14.9%和生物质燃烧13.8%。     

石家庄市春季大气颗粒物的铅污染特征

为了解石家庄市2016年春季大气颗粒物的铅污染特征及来源,利用单颗粒气溶胶质谱仪(SPAMS),分析了大气中含铅颗粒的化学成分。结果表明: 研究期间大气环境中含铅颗粒数浓度共出现11次跳跃式升高,跳跃时间段内石家庄均处于轻度污染过程。从成分分析来看,含铅颗粒分为纯铅颗粒、Pb与K(Pb-K)、OC(Pb-OC)、Cl(Pb-Cl)、混合颗粒等八大类。观测结果表明:Pb-K颗粒最多,占到含铅颗粒的84.4%；其次为纯铅颗粒,占比为13.0%。与石家庄市污染源谱库比对进行来源解析,得到Pb-K颗粒主要来自生活垃圾焚烧源, 纯铅颗粒主要来自工业源。结合石家庄市大气污染源排放清单和后向气流轨迹分析,推测含铅颗粒可能来自市区西南方向某区县的生活垃圾焚烧企业。     

2013-2017年京津冀区域PM2.5浓度变化特征

利用2013-2017年京津冀区域13个城市PM_2.5监测数据，综合探讨了该区域PM_2.5浓度的时空变化特征。结果表明：京津冀区域PM_2.5污染整体较重，但治理成效显著，2013-2017年区域PM_2.5年均质量浓度分别为106、93、77、71、64 μg/m³，完成《大气污染防治行动计划》PM_2.5浓度下降25%左右的目标；13个城市PM_2.5浓度各百分位数总体呈现下降趋势，且随百分位数增大而下降速率加大，PM_2.5年均质量浓度平均每年下降10.6 μg/m³，污染严重的太行山沿线城市邢台、石家庄、邯郸3个城市平均每年分别下降20.3、16.1、13.9 μg/m³；京津冀区域PM_2.5重度污染天数比例分别为19.9%、16.6%、9.5%、9.0%、7.0%，呈下降趋势。2013-2017年京津冀区域PM_2.5平均质量浓度与非重度污染天相比升高19 μg/m³，PM_2.5重度污染天平均质量浓度较非重度污染天时高244.4%。     

近15a江苏省水源涵养功能时空变化与影响因素探析

定量评估生态系统服务功能是合理利用自然资源、促进人地关系与可持续发展的基础。以江苏省为研究区，基于InVEST模型产水模块，定量分析了江苏省2000~2015年产水功能和水源涵养功能的时空变化特征，并采用回归分析和主成分分析法评估了水源涵养功能与社会经济要素之间的关系。结果表明：（1）江苏省多年平均产水量为571×108 m³/a，水源涵养总量78.39×108 m³/a；（2）2000~2010年江苏省水源涵养功能呈下降趋势，2010~2015年有所回升，考虑到降水因素，江苏省实际水源涵养功能持续降低；（3）土地利用变化使15a来水源涵养功能下降15.2×108 m³，降低幅度为16.9%；（4）流域尺度上水源涵养功能与江苏社会经济因素相关关系不显著。研究结果可以为土地利用优化、流域水资源管理提供科学参考和支持。     

汉丹江(陕西段)水质变化特征分析

基于2008—2012年陕西省境内汉江、丹江干流14个断面的水质监测数据,采用单因子评价法、平均综合污染指数法和秩相关系数法等对该段水体的水质变化特征进行研究。结果表明,汉丹江(陕西段)断面水质以Ⅱ类、Ⅲ类为主,水质总体为优;水质综合污染状况呈下降趋势,其中汉江下降趋势显著;水质综合污染状况空间差异和区域分布特征明显,城区段污染大于郊区,各行政区段河流下游污染大于上游。流域水质主要受有机污染和营养盐因子影响,水体污染源主要来自城镇生活源和农业面源,工业源占比不大且排放行业较为集中。     

杭州市环境空气中黑碳质量浓度变化特征

采用2013年环境空气自动监测数据,分析杭州市空气中黑碳质量浓度的变化规律,并对变化特征的产生原因进行探讨。结果表明:黑碳测定年均值为4.10μg/m3,日变化有明显双峰结构,峰值出现在早7时和晚8时左右;从季节看,黑碳质量浓度冬季高(5.20μg/m3)、夏季低(3.00μg/m3);黑碳质量浓度与NO2、CO、PM10、PM2.5显著相关,与O3、风速、气温呈负相关,降水对黑碳的清除作用明显。     

乌鲁木齐市近年来大气降尘变化规律及空间分布特征

基于乌鲁木齐市多年监测的降尘数据,对乌鲁木齐市城区的降尘量进行了统计分析,得出了乌鲁木齐市近10年来降尘的总体变化规律;利用方差分析方法检验了不同季节降尘的显著性差异,发现采暖期的降尘量明显大于非采暖期;按行政区分析降尘量的分布变化,结果为不同行政区存在差别;利用秩相关系数法对其进行了趋势分析。结果表明,乌鲁木齐市的降尘呈下降趋势。利用Arc GIS9.3中的普通克里格插值方法绘制了乌鲁木齐市城市自然降尘的空间分布图。     

A novel driver hazard perception sensitivity model based on drivers’ characteristics: A simulator study

Objective: Considering the high annual number of fatal driving accidents in Iran, any approach for reducing the number or severity of driving accidents is a positive step toward decreasing accident-related losses. Accidents can often be avoided by a timely reaction of the driver. One of the steps before reacting to a hazard is perception. Some driver characteristics may affect road hazard perception. In this research, it was assumed that various driver characteristics, including demographic characteristics and cognitive characteristics, have an impact on driver perception.

Methods: The driving simulator used in this research provides various scenarios; for example, passing a pedestrian or animal across the road or placing fixed objects in a 2-lane separated rural road for 2 groups of experienced and inexperienced drivers under day and night lighting conditions. The go/no-go test was carried out in order to assess drivers’ attention to driving tasks and inhibitory control. A structural equation model (SEM) was used to estimate the relation between driver characteristics and sensitivity to road hazard perception. A new hazard perception index was proposed based on the time intervals in the hazard vulnerability.

Results: The results show that the most effective variables in the analysis of sensitivity to hazard perception are driving experience (in kilometers) during the last 3 years and road lighting conditions. Moreover, hazard perception sensitivity was improved by better inhibitory control, selective attention, and decision making, more carefulness, the average amount of daily sleep, and marital status.

Conclusion: The results of this research may be useful in educating and advertising programs. It also could enhance sensitivity to perception of hazards such as pedestrians, animals, and fixed obstacles among young and novice drivers.     

2014年北京市CO浓度水平和时空分布

对北京市地面监测站点的CO浓度进行分析，探讨其浓度水平、变化趋势和时空分布特征。2014年春、夏、秋、冬四季北京市CO平均浓度分别为1.06、0.87、1.34、2.17 mg/m³。CO浓度均呈双峰型变化，第一个峰值出现在07：00-09：00，主要由交通早高峰的排放引起；第二个峰值出现在23：00左右，主要受交通晚高峰排放和夜间边界层高度降低的挤压效应的共同影响。从空间分布来看，全年整体呈现南高北低的分布特征，尤其是秋、冬季较为明显，体现了工业布局和区域传输对CO的影响。从全年来看，湿度对CO浓度的影响最大。对2014年冬季北京市的一次高CO浓度分析结果表明，此次过程是由本地排放和区域传输共同造成的，气象要素中地面气压对CO浓度影响最大。