日本汽车排放法规对PM排放的技术要求研究

本文介绍了日本轻型和中型机动车PM排放要求的发展历程,重点分析了日本现行汽车排放法规对PM排放测量的技术要求,并对日本汽车PM排放要求的发展趋势进行了总结。通过对日本汽车排放法规PM排放要求的研究,对我国进一步完善相关排放法规内容及进行汽车出口认证具有积极的参考意义。     

广州市高架路两侧PM2.5分布特征研究

针对广州市琶洲大桥北段高架路,本文通过实测分析了高架路两侧PM_(2.5)质量浓度的分布特征,解析了车流量、车型比例、风速风向、温湿度等因素对PM_(2.5)浓度变化的影响,同时建立了高架路机动车尾气排放与扩散模型,并通过模拟值与实测值的对比阐明了误差原因。结果表明:在水平方向上,PM_(2.5)浓度随着与高架路面距离的增加而减小,在垂直方向上,PM_(2.5)浓度随着垂直高度的下降先升高后降低。影响因素分析表明,车流量影响较小,而货车比例、风速风向、温湿度对于PM_(2.5)浓度分布的影响较大。利用实测数据验证了高架路机动车尾气排放与扩散模型能够较好地预测高架路PM_(2.5)浓度的空间分布特征。     

南京港大气PM10污染特征与一次组分来源解析研究

为研究污染源排放对港区大气环境的影响,项目选取了典型内河港-南京港区监测点,分别于夏季与冬季代表月进行大气PM10样品的采集,并分别用ICP-MS、离子色谱、热光碳分析仪进行了元素、离子与OC/EC等化学组分的分析。结果表明,夏、冬两季采样期间南京港大气PM10质量浓度平均值为88.1±31.9μg/m~3、136.9±76.8μg/m~3。夏、冬两季二次无机离子浓度分别为25.4μg/m~3与40.8μg/m~3,分别占总PM10的28.9%与31.1%;夏季NO_(3-)最高,冬季SO_(42-)最高。夏、冬季碳质组分浓度差异较大,分别为21.5μg/m~3与43.7μg/m~3,占比分别为24.4%与39.7%;一次排放是碳质组分的主要来源,夏冬两季的差异主要来自于一次碳质组分。一次组分来源解析结果显示,土壤尘与冶金、机动车、燃煤、海盐与船舶是南京港大气PM10的主要贡献源,土壤尘最高,约为33.0%,海盐与船舶贡献分别约为6.4%和6.6%。冬季的海盐与船舶排放贡献略高于夏季。     

拿什么来拯救北京的空气

<正>一个幽灵,代号为PM2.5的幽灵在北京上空徘徊。此幽灵来无影去无踪,看不见闻不到,却害人于无形之中。PM2.5就是空气中粒径在2.5微米以下的可吸入颗粒物。与粒径较大的PM10相比,PM2.5对人体健康的伤害更大,其被吸入人体后会进入支气管,干扰肺部     

驾校门槛渐趋市场化--《机动车驾驶员培训机构资格条件》等两项国家标准将实施

千呼万唤始出来的《机动车驾驶员培训机构资格条件》和《机动车驾驶员培训教练场技术要求》两项国标,牵动着1万多所驾校的小心脏。按照修订方的说法,标准升级了旧版本,升华了准入条件,有利于规范机动车驾驶员培训市场经营秩序,提升培训质量与服务水平,促进行业节能减排,切实加强驾培市场的源头管理。     

江门市机动车排放清单及控制策略研究

文章结合机动车实际活动水平和排放模型,建立了江门市机动车污染物排放清单。江门市机动车排放污染物共计8.1万吨,主要排放物为CO和NO_X,CO和HC的排放以摩托车为主,NO_X与PM2.5排放以柴油车为主,低排放标准车型的排放量较高。根据江门市机动车排放清单,建议加强摩托车的管控措施,淘汰高排放汽车,完善城市公共交通系统,大力推广新能源汽车。     

“大数据”助力交通治污减排——京津冀首个交通节能减排实验室成立

<正>"据环保部门测算,北京本地PM2.5污染中,机动车排放占比达31.1%,已成为首要污染源。交通已是三大重点能耗与大气污染物排放领域之一,亟需构建绿色可持续的现代交通体系。治污具体从哪入手?京津冀地区首个交通节能减排实验室的建成,通过对"人、车、路、环境"交通全要素的检测,或为治霾提供新思路。"今年冬天,雾霾席卷大半个中国,尤其北京、河北等地的PM2.5浓度指数爆表。作为大气的重要污染源之一,机动车尾气再次成为社会关注的焦点。但机动车排放、交通与PM2.5究竟是何关系,仍缺乏权威论证。     

甘肃高速公路施工期大气环境污染控制分析

为进一步了解甘肃高速公路施工期大气环境污染及其防治对策,作者分别于2016年与2017年的夏季在某高速公路的五个拌和站进行了PM10、TSP采样分析,以期为甘肃高速公路施工期大气环境污染控制措施提供参考。     

反映车辆污染物排放水平的指标研究

机动车排放受车辆类型、车龄、燃料类型、油品质量等多种因素的影响,这些因素也直接或间接反映车辆的污染物排放水平。本文系统梳理了反映单个车辆污染物排放水平的指标,并分为直接和间接指标两类,其中车辆的燃料类型、车辆大小、车龄、行驶里程、行驶速度以及车辆满足的环保排放标准等指标是间接指标,间接反映车辆的排放水平。车辆实时尾气排放检测结果是直接指标,能够直接反映车辆的污染物排放水平。反映车辆污染物排放水平的指标研究是车辆污染物排放研究的基础性研究,也是机动车污染减排的重要支撑。     

江东大桥第二合同PM26墩承台双壁钢套箱围堰施工

杭州市江东大桥第二合同PM26自锚墩为浅水低桩大体积混凝土承台基础,位于钱塘江主河槽.采用无底双壁套箱围堰施工承台,速度快,质量优,效益好.     

Efficiency evaluation and PM emission reallocation of China ports based on improved DEA models

The emission of particulate matter (PM) and other atmospheric pollutants in port operations and shipping have caused a huge negative impact on the environment. Consequently, how to evaluate the environmental efficiency of ports and put forward corresponding countermeasures on this basis is becoming a more important issue than ever before from the perspectives of the government, academia, and society. We construct three data envelopment analysis (DEA) models to evaluate the environmental efficiency of ports under the circumstances of environmental control, non-environmental control and PM emission through inter-ports cooperation. The innovation of the DEA models constructed in this paper lies in: (1) Setting environmental control parameters to uniformly manipulate the situations of environmental control and non-environmental control, etc.; (2) Allowing non-equal proportional change of input index, expected output and non-expected output index; (3) Setting preference coefficients for ports to admit favorable decisions; (4) Providing a distance formula of expected output for PM emission reallocation. Further, data from 11 major ports in China are collected to compare the expected output under different PM emission standards assuming the situation of environmental control and non-environmental control, port cooperation, and non-cooperative sewage discharge. The empirical results show that: (1) Ports in the eastern China (Shanghai, Ningbo, and Nanjing) have higher port efficiency; (2) Port cooperation can improve the overall expected output but it will lose its edge with the improvement of PM emission standards. (3) Ports follow the same trend of output loss regardless of favorable decisions. In the end, the author makes a summary, puts forward relevant policy suggestions and makes the recommendation for future research.     

Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and video cameras were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6–9 am), midday (11–2 pm), and afternoon peak (3–6 pm) on a weekday (June 23, 2016) along a high-volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the remote lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds, and average speeds were closer after model calibration. Traffic emissions of nitrogen oxides (NOx) and particulate matter (PM₁₀ and PM_2.5) were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NO_x: 4.0–27.0 g; PM₁₀: 0.3–4.8 g; PM_2.5: 0.2–1.3 g) for the corridor. The estimates based on radar speeds were at least three times lower than emissions derived from simulated vehicle trajectories. Finally, the PM₁₀ and PM_2.5 near-road concentrations derived from emissions based on simulated speeds were two or three times higher than concentrations based on emissions derived using radar data. Our findings are relevant for project-level emission inventories and PM hot-spot analysis; caution must be exercised when using raw radar data for emission modeling purposes.     

Preliminary investigation of PM1, PM2.5, PM10 and its metal elemental composition in tunnels at a subway station in Shanghai,China

In this study, real-time monitoring campaigns were conducted in two tunnels (Line A and Line B) at a subway station in Shanghai, including temperature, relative humidity, PM₁, PM_2.5 and PM₁₀, in order to understand the climate and PM characteristics in the transportation microenvironment. In addition, collected floor dust particles in the tunnel were analyzed by ICP for their metal elemental composition. Strong correlations occurred between all PM levels and meteorological parameters in the tunnel of Line A (with platform screen doors), in comparison with the weak correlations between such parameters in the tunnel of Line B (without platform screen doors). PM_2.5 and PM₁₀ between peak hours and off-peak hours for both lines presented significant differences (p < 0.05), respectively. Nevertheless, PM₁ showed a different pattern, with p > 0.05 for Line A and p < 0.05 for Line B, respectively. In addition, statistical results concluded that PM had an evident weekly variation for both lines. Friday was the highest day of all particulate matters in monitoring periods for both lines. Ratios of PM₁/PM₁₀ and PM_2.5/PM₁₀ were high when trains were out of service and low when trains were in service. Relative abundance of metal elements detected from floor dust particles proved that floor dust particles in tunnels might be a major source of airborne PM in the subway microenvironments, with Fe as the most abundant metal element, followed by Ca, Al, Mg, Mn, Zn, Cu, Cr, Ni, Pb and Hg.     

The contribution of road traffic to particulate matter and metals in air pollution in the vicinity of an urban road

A detailed investigation was conducted to study the sources of particulate matter in the vicinity of an urban road in Žilina. To determine the amount of particulate matter (PM₁₀, PM_2.5 and PM₁) present in the ambient air, a reference gravimetric method was used. The main objective of this contribution was to identify the sources of these particles by means of statistical methods, specifically principal component analysis (PCA), factor analysis (FA), and absolute principal component scores (APCS), as well as using the presence of 17 metals in the particulate matter (Na, Mg, Al, Ca, V, Cr, Fe, Mn, Ni, Cu, Zn, As, Mo, Sb, Cd, Ba, Pb). To identify the metals in the particulate matter samples and to determine their abundances, spectroscopic methods were used, specifically inductively coupled plasma mass spectrometry (ICP-MS). Each of these metals may come from a specific source, such as the burning of fossil fuels in fossil fuel power plants; local heating of households; the burning of liquefied fossil fuels in the combustion engines of vehicles; the burning of coal and wood; non-combustion related emissions resulting from vehicular traffic; resuspension of traffic-related dust; and industry. Diesel vehicles and non-combustion emissions from road traffic have been identified as two key sources of the particulate matter. The results reveal that non-combustion emissions, which are associated with the elements Na, Fe, Mn, Ni, Zn, Mo, Sb, Cd, and Pb, are the major contributors, followed by combustion emissions from diesel vehicles, which are associated with the elements Mg, Ca, and Ba.     

Personal exposures to PM during short distance highway travel in India

Traffic is the largest contributor (37%) to urban air pollution in India. During commuting, passengers are significantly exposed to pollutants. We carried out a study on a National Highway (NH) in India to measure personal exposure to Particulate Matter (PM) in five travel modes. PM_2.5 concentrations showed the following trend: Bus > Car FA (fresh air mode of air condition) > Bus AC > Car > Car RC (re-circulation mode of air condition). Highest and lowest concentrations of PM₁₀ were observed in Bus (134 ± 47 µg m⁻³) and Car RC (20 ± 5 µg m⁻³), respectively. The exposures were highest at the rear seats during the Bus AC journeys. In Car FA, the contribution of PM₁ to total concentrations was dominant (61%). Travel modes explained highest variabilities in PM₁₀, PM_2.5 and PM₁ concentrations. In all travel modes, the highest particle counts were observed for PM_0.3–0.5. PM_>0.5–5.0 counts during Bus journeys were comparatively higher than remaining modes. Deposition doses of passengers were as high as 3.22 µg of PM₁₀ (in Bus), 0.66 µg of PM_2.5 (in Bus) and 0.06 µg of PM₁ (in Bus AC) during the ~1 h journey. Our study revealed that Car RC is the safest mode of travel, both in terms of personal exposures and PM depositions in respiratory system. The results from this study can be used to target efforts to reduce personal exposure of highway commuters.     

Did Chinese cities that implemented driving restrictions see reductions in PM10?

Imposing driving restrictions is becoming increasingly popular as a policy intended to control urban air pollution. Existing studies on this topic offer highly mixed observations, and each study tends to focus on only one city. In this paper, we used 11 Chinese cities with driving restrictions as the treatment group, and compared them to other cities that did not implement the policy. Based on a propensity score matching and difference-in-difference analysis, we found no evidence of a decrease in PM10 concentrations in cities after they implemented driving restrictions. This finding may be attributed to an increase in the number of cars in these cities after implementing driving restrictions, but we also found no evidence of an improvement in air quality for a given number of cars after implementation of the policies.     

项目管理软件现状及其在索道工程中的应用探讨

介绍了项目管理软件的发展过程和应用现状，并就典型项目管理软件的主要功能和特点进行了介绍，结合索道工程项目的特点，就索道行业应用项目管理软件提出了一些建议。     

The nexus between PM 2.5 and urban characteristics in the Texas triangle region

Particulate matter (PM), coming from various human activities involving the burning of fuels, has many negative effects on human health. Through measurements of PM data at sparsely distributed monitoring stations across a given area, many studies have examined the simple relationship between PM and either of two urban characteristics: land cover and transportation. However, the studies of PM data from a limited number of monitoring stations have not fully accounted for variations in regional PM concentration. Furthermore, consideration of only one of two key urban characteristics may not provide a complete picture of the relationship. Hence, the primary goal of this study is to estimate the effects of both land cover and transportation on the PM 2.5 concentration indicated by satellite imagery. Focusing on the Texas Triangle region, we implemented diverse transportation measures with Geographic Information System (GIS) techniques and land cover measures with remotely sensed imagery at the census tract level. With these measures, we developed spatial regression models to examine spatially correlated effects on PM 2.5. We then used the estimated models to conduct elasticity analysis, thus helping to design an environmental policy to alleviate PM 2.5 and achieve long-term regional sustainability.     

Air quality impact assessment of NOx and PM due to diesel vehicles in Delhi

The impact of diesel vehicles on NO_x and PM emissions at various locations in Delhi is assessed using two line source models; the California line source version 4 and the Indian Institute of Technology Line Source. The models offer comparable results but both under predicting the observed values with the Indian Institute of Technology model predictions being slightly better. The analysis also identifies hotspots due to concentrations of NO_x and PM and their diurnal variations is found to be greater in at night hours.