Overestimation of self-reported driving exposure: Results from the SHRP2 Naturalistic Driving Study

Objectives: The accuracy of self-reported driving exposure has questioned the validity of using self-reported mileage to inform research questions. Studies examining the accuracy of self-reported driving exposure compared to objective measures find low validity, with drivers overestimating and underestimating driving distance. The aims of the current study were to (1) examine the discrepancy between self-reported annual mileage and driving exposure the following year and (2) investigate whether these differences depended on age and annual mileage.

Methods: Two estimates of drivers’ self-reported annual mileage collected during vehicle installation (obtained via prestudy questionnaires) and approximated annual mileage driven (based upon Global Positioning System data) were acquired from 3,323 participants who participated in the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study.

Results: A Wilcoxon signed rank test showed that there was a significant difference between self-reported and annual driving exposure during participation in SHRP 2, with the majority of self-reported responses overestimating annual mileage the following year, irrespective of whether an ordinal or ratio variable was examined. Over 15% of participants provided self-reported responses with over 100% deviation, which were exclusive to participants underestimating annual mileage. Further, deviations in reporting differed between participants who had low, medium, and high exposure, as well as between participants in different age groups.

Conclusions: These findings indicate that although self-reported annual mileage is heavily relied on for research, such estimates of driving distance may be an overestimate of current or future mileage and can influence the validity of prior research that has utilized estimates of driving exposure.     

A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

Community evacuation following a chlorine release, Mississippi

On 7th September 1986, four miles north of Collins, Mississippi, a train transporting chlorine derailed. Two cars ruptured and gas escaped. As a result, 100 families were evacuated. To study the evacuation process, we conducted person-to-person interviews with sixty-two families staying in the evacuation center. Only 52.5% of the families received their first directive to evacuate directly from police or other officials. Delays in evacuating tended to be shorter when people were warned by the police and were told the reason for evacuating. Lack of personal transportation and preexisting health problems resulted in delays in evacuation. Concerns about evacuation included fear of looting, lack of a place to go, lack of transportation, difficulty in moving with children and elderly persons, and the need to take care of pets. One third of the interviewees reported feeling panic. Community evacuation procedures would be improved if: (1) officials contact all households directly; (2) the warning message addresses people's concerns; and (3) transportation is provided.     

北京市农业机械排放因子与排放清单

利用车载排放测试技术对典型的联合收割机、拖拉机、农用运输车和农田建设机械实际工况下的尾气进行测试,建立了实际工况下农业机械的排放因子和2017年北京市农用机械排放清单.结果表明,不同的工作状态对农业机械尾气排放有较大的影响,怠速和行走时CO、NO_x、HC和PM排放趋于平稳;而切地和翻地模式下的波动较为明显.根据各类机械的分类和排放标准对排放因子进行细化,建立了较为完整的实际工况下的排放因子.根据农业机械排放因子和燃油消耗量计算出2017年北京市CO、NO_x、HC和PM的排放量分别是2 566.60、 1 239.29、 563.08和538.32 t.拖拉机、运输机械和联合收割机的污染物总量占CO、NO_x、HC和PM这4种污染物总量的98%、 95%、 95%和98%.因此,农用拖拉机、运输机械和联合收割机在农业机械污染减排中应作为重点控制对象.     

基于腔衰减相移光谱法的二氧化氮分析仪的设计与应用

基于腔衰减相移光谱法设计了一套二氧化氮在线分析仪,通过优化测量参数,该仪器可长期稳定运行,其时间分辨率为60 s,检出限为0.191 ppb,在0~300 ppb范围内,NO₂气体浓度与相位正切信号值具有较好的二次拟合关系,R²为0.9995.另外,该仪器在泰安站进行了长期外场观测,并与改装后的进口商品化仪器PKU-Thermo 42i-TL进行比对实验,结果表明,两者的测量结果一致性较好,R²=0.9811,表明其具有良好的运行稳定性和测定结果准确性,适用于环境大气二氧化氮浓度的在线监测.外场观测结果表明,春季泰安站二氧化氮浓度均值为12.39 ppb,有明显日变化规律.     

深圳大气PM2.5中水溶性有机物的来源特征

对2017年9月~2018年8月深圳市北部大气PM_2.5中水溶性有机物（WSOM）的质量浓度、质谱及来源结构进行测量和分析.结果表明：PM_2.5的质量浓度为（32.3±18.4）μg/m³,WSOM的质量浓度为（9.4±5.7）μg/m³,占颗粒物总有机物的（77.6%±14.0%）.质谱分析显示,WSOM的氧碳比（O/C）平均值达到（0.57±0.09）,属于二次有机物的O/C值范围,且生物质燃烧排放的离子碎片C₂H₄O₂⁺的丰度显著,说明WSOM的来源中有显著的生物质燃烧排放的有机气溶胶.为了明确WSOM的来源结构,利用正矩阵因子分解法（PMF）模型进行来源解析,发现3个合理因子：高氧化态有机气溶胶（MO-OOA）,低氧化态有机气溶胶（LO-OOA）和生物质燃烧（BBOA）,贡献比例分别为51.7%,31.8%和16.5%.MO-OOA和BBOA贡献浓度均呈现秋冬高、春夏低的季节变化特征,反向轨迹分析显示其与内陆污染传输关系密切.LO-OOA的变化相对稳定,本地源的贡献较大.结合¹⁴C同位素示踪法对秋冬季WSOM样品分析,发现机动车等化石源二次有机物是WSOM的主要来源,贡献比例达到53.9%,需继续加强对化石燃料控制来降低WSOM污染.     

DOC+CDPF对国III重型柴油货车实际道路排放的影响

利用便携式车载排放测试系统（PEMS）对2辆加装氧化催化转化器（DOC）和催化型柴油颗粒捕集器（CDPF）与否的国III重型柴油货车进行实际道路排放测试.结果表明,2辆改造重型柴油车的CO、THC、固态颗粒物粒数（SPN）和黑碳（BC）实际道路排放因子分别为（1.31±0.37）g/（kW×h）、（0.20±0.03） g/（kW×h）、（7.13×10¹⁰±5.27×10¹⁰）个/（kW×h）和（0.69±0.06）mg/（kW×h）,相对于原始排放（拆除DOC+CDPF）分别降低52.48%、55.69%、99.91%和99.22%.从低速、中速到高速,CO和THC减排比例呈现上升趋势,然而运行工况对SPN和BC减排比例则无显著影响.加装DOC+CDPF会导致NO₂在NO_x中的占比升高,且从低速、中速到高速涨幅依次增大,但对NO_x无明显减排效益,其排放因子为9.53~9.83g/（kW×h）,远高于实验室排放限值.     

水体及沉积物氮磷水平对附植藻类的影响

为了探讨湖泊富营养化过程中沉积物及水体氮、磷浓度对附植藻类的影响,通过室内模拟实验,研究了水体及沉积物氮、磷升高对苦草(Vallisnerianatans(Lour.) Hara)上附植藻类生长、群落组成及其体内氮、磷含量的影响.结果表明,在实验条件下,随着水中氮、磷含量升高,附植藻类生物量及附植藻类氮、磷含量均呈极显著增加(p0.01).随着水体可获得的氮、磷浓度升高,附植藻类的相对丰度有所变化,舟形藻(Navicula)、小球藻(Chlorella)及微囊藻(Microcystis)相对丰度随着氮、磷水平的升高而下降,直链藻(Melosira)则相反,但舟形藻、直链藻、微囊藻、小环藻(Cyclotella)和小球藻均为群落的优势属种.沉积物氮、磷含量升高对附植藻类生物量、优势种丰度及群落氮、磷含量影响较小,均未达到显著水平(p0.05).在实验条件下,沉积物氮、磷含量对附植藻类影响不大,而水体氮、磷浓度升高显著地促进了附植藻类生长.研究结果也为解释富营养化湖泊沉水植物衰退及消亡提供了一定的科学依据.     

Development of monitoring technology for airborne particulate matter

Particulate matter suspended in the air has adverse effects onhuman health. Its level of concentration is an important parameter in evaluating the degree of hazard it poses to the atmosphere. Conventional methods used in measuring particulatematter are often filter-based, which indicates some disadvantagesbecause such a base requires labor and time. In this study, to achieve real-time measurements, a new electrical method was developed for measuring PM10 and PM2.5 concentrations. The basicprinciple is to electrically charge particles passing through thePM inlet using a corona charger and measure the currents createdby charged particles to obtain the number concentration of particulate matter. A new type inlet based on the particle cupimpactor configuration was designed and its performance was evaluated. A unipolar diffusion charger was developed and thecharger's efficiency was determined experimentally in terms ofPn, which represents the penetration through the charger,P, times the average charge number acquired by a particle,n, for different particle sizes. The correlation was constructed between the PM10 (or the PM2.5) mass concentrationsand the electrical currents due to particles, which were chargedby the diffusion charger.