天津市春季典型道路积尘负荷分布特征

道路积尘负荷表征路面清洁程度,是道路扬尘排放清单中一个十分重要的参数,也是各地环境管理的一个重要方面。根据AP-42道路扬尘排放因子模型,2015年春季用真空吸尘法采集了天津市11条道路的扬尘样品,得到了不同道路类型以及不同车道的积尘负荷,并分析了积尘负荷的变化规律。结果表明:天津市春季非机动车道和机动车道慢车道的积尘负荷分别为0.282 0~1.064 5 g/m~2和0.050 4~0.173 9 g/m~2;5种道路类型的非机动车道的积尘负荷中位值均大于机动车慢车道积尘负荷的中位值;对于不同道路类型积尘负荷而言,非机动车道从大到小顺序依次为次干道主干道环线支路快速路,机动车道慢车道从大到小顺序依次为次干道环线主干道支路快速路;东西走向和南北走向道路两侧积尘负荷差异均无统计学意义。     

杭州城区PM2.5和PM10污染特征及其影响因子分析

利用2013年12月—2014年11月杭州城区空气质量监测站PM_(2.5)、PM_(10)浓度值结合气象、道路、人口数据以及站点周边绿地信息分析PM_(2.5)、PM_(10)浓度时空特征及其影响因子。结果表明,杭州城区各监测站PM_(2.5)和PM_(10)晴天日浓度变化趋势基本一致,PM_(2.5)比PM_(10)污染严重;晴天日PM_(2.5)、PM_(10)浓度值与对应的温度(-0.463,-0.281)、风速(-0.305,-0.332)呈负相关,与湿度(0.257,0.239)呈正相关;晴天有风时,杭州市区PM_(2.5)、PM_(10)污染北部重于南部,东部重于西部,浓度极高值集中在风速小于5 m/s时段,且风速越小浓度值越高;温度为12℃左右,湿度在60%~80%时,颗粒物污染最严重;交通高峰时各监测站PM_(2.5)、PM_(10)污染程度存在明显差异。相关性分析表明,PM_(2.5)、PM_(10)污染程度与道路密度成正比,与缓冲区内绿地覆盖面积成反比。PM_(2.5)污染程度与人口密度成正比,PM_(10)污染与人口密度成反比。     

南京市快速道路交通噪声污染调查

对南京市城西干道凤台路、虎踞北路双层高架道路交通噪声进行了测量.结果表明,城西干道两侧30m范围内夜间噪声严重超标.建议今后在建设城市快速道路时,地面路段两侧可种植绿化隔离带,高架路段设置隔声屏障;不具备条件的可为临街住户安装隔声门窗,以保护道路两侧的居住环境.     

上海市交通干道空气中苯系物冬季污染特征初探

2004年冬季对上海市交通干道附近空气中苯系物的浓度水平进行了监测,并采用气相色谱法进行分析。结果表明,采样期间大气中苯、甲苯、乙苯、二甲苯的浓度分别为1.77~27.7μg/m3、7.29~195μg/m3、3.11~40.2μg/m3、4.49~82.4μg/m3。每日6:30~9:30和15:30~19:00两个时段苯系物的浓度要高于中午时间的浓度,与国内其他城市相比,上海市甲苯的浓度要高,浓度水平要远远高于国外城市的测定结果。苯系物的浓度受风速和风向影响较大。提出了制订空气中苯系物的排污清单和加强机动车尾气中苯系物控制的建议。     

有色冶炼园区道路扬尘中重金属污染特征及健康风险评价

为研究有色冶炼工业园区周边道路扬尘中重金属污染特征及其健康风险,在云南省蒙自地区采集了城市道路、有色冶炼工业园区道路以及隧道尘样品,通过再悬浮设备将尘样悬浮至Teflon滤膜上获得PM_2.5和PM₁₀样品,并利用ICP-MS分析了Cr、Mn、Ni、Cu、Zn、As、Cd和Pb这8种重金属的含量.结果表明,在PM_2.5中重金属的平均含量高于PM₁₀.Pb、Cd、As和Zn在3种道路扬尘中平均含量最高,且在不同道路扬尘中平均含量差异表现为:隧道>工业园区道路>城市道路.隧道扬尘中Pb和As的平均含量高于其它重金属,在PM_2.5中达到92 338.3 mg·kg^-1和12 457.7 mg·kg^-1;工业园区道路扬尘中Pb和Zn的平均含量最高,在PM_2.5中分别是4 381.7 mg·kg^-1和4 685.0 mg·kg^-1;城市道路平均含量最高的重金属是Zn和P...     

A clustering approach to integrate traffic safety in road maintenance prioritization

Objectives: We combine data on roads and crash characteristics to identify patterns in road traffic crashes with regard to road characteristics. We illustrate how combined analysis of data regarding road maintenance, maintenance costs, road characteristics, crash characteristics, and geographical location can enrich road maintenance prioritization from a traffic safety perspective.

Methods: The study is based on traffic crash data merged with road maintenance data and annual average daily traffic (AADT) collected in Denmark. We analyzed 3,964 crashes that occurred from 2010 to 2015. A latent class clustering (LCC) technique was used to identify crash clusters with different road and crash characteristics. The distribution of crash severity and estimated road maintenance costs for each cluster was found and cluster differences were compared using the chi-square test. Finally, a map matching procedure was used to identify the geographical distribution of the crashes in each cluster.

Results: Results showed that based on road maintenance levels there was no difference in the distribution of crash severity. The LCC technique revealed 11 crash clusters. Five clusters were characterized by crashes on roads with a poor maintenance level (levels 4 and 3). Only a few of these crashes included a vulnerable road user (VRU) but many occurred on roads without barriers. Four clusters included a large share of crashes on acceptably maintained roads (level 2). For these clusters only small variations in road characteristics were found, whereas the differences in crash characteristics were more dominant. The last 2 clusters included crashes that mainly occurred on new roads with no need for maintenance (level 1). Injury severity, estimated maintenance costs, and geographical location were found to be differently distributed for most of the clusters.

Conclusions: We find that focusing solely on road maintenance and crash severity does not provide clear guidance of how to prioritize between road maintenance efforts from a traffic safety perspective. However, when combined with geographical location and crash characteristics, a more nuanced picture appears that allows consideration of different target groups and perspectives.     

Spatial analysis of mortality rate of pedestrian accidents in Iran during 2012–2013

Objectives: Considering the high mortality rate of pedestrians in traffic accidents in Iran, the present study aimed to determine the high-risk and low-risk areas of accidents resulting in pedestrian deaths and the spatial analysis of their mortality rates.

Methods: This cross-sectional study included 4,371 deceased pedestrians reported by the Legal Medicine Organization in Iran from March 2012 to March 2013. For spatial analysis, the collected data were entered into ArcGIS software version 10.2 and a spatial map of the mortality rate was drawn according to the distribution of data in the provinces. Using this software, high-risk and low-risk areas were identified by calculating the spatial autocorrelation of the data. The Moran’s index of road accident patterns was surveyed and high-risk and low-risk points were identified using the local Getis index.

Results: The age-standardized incidence rate was 6.8 per 100,000. After analyzing the data using ArcGIS software, the local Moran’s index showed a cluster pattern with a high mortality rate in 3 provinces of Mazandaran, Gilan, and Qazvin. In identifying high-risk and low-risk points, the local Getis index showed 3 hot spots with a confidence interval of 99% in Qom, Qazvin, and Mazandaran and 5 hot spots with a 95% confidence interval in Markazi, Tehran, Zanjan, Gilan, and Golestan provinces.

Conclusions: According to the cluster pattern of accidents in the 3 provinces and the presence of hot spots in 9 provinces, it is necessary to identify factors that increase the risk of death in the study provinces in order to reduce the mortality rate among pedestrians due to traffic accidents. Therefore, to reduce the pedestrian mortality rate, especially in high-risk provinces, some studies need to be conducted to determine the risk factors in pedestrian mortality.     

Pollution status,ecological risk assessment and source identification of heavy metals in road dust from an Industrial Estate in Trinidad,West Indies

Road dust in an industrial estate could potentially contain toxic heavy metals as a result of various anthropogenic sources. This study investigated sixteen samples from various locations throughout the Point Lisas Industrial Estate in Trinidad, West Indies. Samples were acid-digested and analysed by Flame Atomic Absorption Spectroscopy, which revealed that concentrations for Cd, Cr, Cu, Mn, Ni, Pb and Zn ranged from 2.48–5.45?μg/g, 14.2–70.5?μg/g, 15.3–130?μg/g, 219–1330?μg/g, 20.0–62.1?μg/g, 43.5–113?μg/g and 105–1154?μg/g, respectively. Different methods (Geoaccumulation index, pollution index, integrated pollution index, enrichment factor and ecological risk assessment) of assessing heavy metal contamination in road dust indicated negligible to moderate degrees of pollution, in which the associated potential ecological risk was considered low. Principal component analysis (PCA) and Cluster analysis (CA) grouped the heavy metals according to potential sources and indicated that the accumulation of heavy metals in the road dust of the Estate were influenced mainly by vehicular and industrial processes.     

深圳市大气中PM2.5载带金属污染特征及健康风险

于2020年3月～2021年2月对深圳市道路环境空气中PM_2.5载带的15种金属元素的质量浓度进行时间分辨率为1h的全年在线观测.结果显示,深圳市道路环境空气中PM_2.5载带金属元素的总平均浓度为(1062.3±434.6) ng/m³,其中Fe、Al、K、Ca和Zn为主要贡献元素,在金属元素中总贡献达到95.5%.Fe的浓度较高,受到与道路扬尘和机动车排放的强烈影响.金属浓度存在着显著的季节性差异,冬季浓度最高(1709.3ng/m³),夏季最低(644.1ng/m³).Mn、Fe、Cr、Zn和Ca元素呈现明显的双峰日变化分布,与机动车流量高峰一致.昼夜浓度分布结果显示,夜间船舶排放V和Ni的浓度高值得关注,而Mn、Zn和Ca的浓度白天较夜晚高,与白天机动车流量较高有关.高污染日总金属日间浓度和夜间浓度均为全年日间和夜间平均浓度的1.9倍.成年人与儿童暴露于深圳市道路环境空气的非致癌风险均低于阈值1,但是总致癌风险(6.5×10^-6)超过阈值10     

基于EnKF排放清单反演方法的关键影响参数评估与优化

以中国一氧化碳(CO)排放反演为例,利用敏感性分析手段评估了集合数目、局地化半径､膨胀因子、观测站点密度和观测数据时间分辨率对排放清单反演的影响.结果表明:站点密度是影响排放反演的最重要参数.在不同站点密度下,反演的中国CO排放总量差异可达34%.同时,站点密度还会影响排放反演对其他参数的敏感性.随着站点密度的降低,排放反演对局地化半径、集合数目和膨胀因子参数变得更为敏感,但对观测数据时间分辨率的敏感性则有所下降.因此在站点稀疏地区,局地化半径是排放反演的主要影响参数,集合数目和膨胀因子次之;但在观测站点密集地区,局地化半径和观测数据时间分辨率是主要的影响参数,而膨胀因子和集合数目的影响相对较小.该研究能够为不同尺度的排放反演开展参数优化提供借鉴.在中国CO排放反演案例(站点密度为1.55个/10⁴km²)中,建议反演参数设置为:集合数目为50、局地化半径为100km、最大似然估计膨胀方案(MLE)、日均或小时观测数据.