上海市汽车更新淘汰减排效果初步评估

选取2009年6月—2010年1月上海市汽车动态数据,讨论汽车污染物排放状况,分析汽车更新淘汰对大气环境的排放贡献影响。结果表明,上海市现行老旧车辆更新淘汰政策可以减少CO、HC、NOx、PM等污染物排放量43 565 t/a。鼓励老旧汽车淘汰更新政策对推进减排工作、改善城市大气环境有重要意义。     

长三角城市群污染输送相互影响的敏感性试验

利用WRF-Chem模式,对2013年11月29日至12月11日长江三角洲地区的严重空气污染事件进行数值模拟,研究长三角核心区不同污染物本地源和外来输送所占比重。分析长三角核心区排放源对本地不同污染物浓度的污染贡献。结果表明,在2013年12月的这一次污染事件中,颗粒物平均本地贡献与外来输送基本比重相当;而SO_2、CO、NH_3、NO_x这4种气体污染物则以本地贡献为主,本地贡献的差异与气体的化学反应活性有关,活性越强本地贡献比重越大。污染过程中12月7日至12月9日00:00为污染最严重的时段,污染物的本地贡献有明显上升。区域间输送的方向和强度与地面风向、风速有紧密的联系。在边界层高度范围内,大部分污染物越往高空本地排放源的贡献越弱,外来输送主导作用增强,而硝酸盐在地面、1 km和1.5 km的本地贡献差异远小于其他污染物。     

水泥厂大气污染物健康风险评价初探

根据大气污染物的环境行为,沿着污染物排放-迁移转化-人体吸收这条途径,提出了以致残调整生命年为最终评价指标的大气污染物人体健康风险定量评价方法.对广东某水泥厂实例分析说明,水泥生产过程排放的各种大气污染物中,NOx的人体健康风险最大,占水泥生产过程所有大气污染物健康风险的43.9%,SO2的人体健康风险最小.     

Nonpoint Source Pollution Assessment of Wujiang RiverWatershed in Guizhou Province,SW China

The amount of pollution from nonpoint sources flowing in the streams of the Wujiang River watershed in Guizhou Province, SW China, is estimated by a geographic information system (GIS)-based method using rainfall, surface runoff and land use data. A grid of cells of 100 m in size is laid over the landscape. For each cell, mean annual surface runoff is estimated from rainfall and percent land use, and expected pollutant concentration is estimated from land use. The product of surface runoff and concentration gives expected pollutant loading from that cell. These loadings are accumulated going downstream to give the expected annual pollutant loadings in streams and rivers. By dividing these accumulated loadings by the similarly accumulated mean annual surface runoff, the expected pollutant concentration from nonpoint sources is determined for each location in a stream or river. Observed pollutant concentrations in the watershed are averaged at each sample point and compared to the expected concentrations at the same locations determined from the grid cell model. In general, annual nonpoint source nutrient loadings in the Wujiang River watershed are seen to be predominantly from the agricultural and meadow areas. The total annual loadings through the outlet of the watershed are 40,309 and 2,607 tons for total nitrogen (TN) and total phosphorus (TP), respectively.     

Study of meteorological aspects and urban concentration of SO2 in atmospheric environment of La Plata, Argentina

This article presents and discusses SO(2) (ppbv) concentration measurements combined with meteorological data (mainly wind speed and direction) for a five-year campaign (1996 to 2000), in a site near an oil refinery plant close to the city of La Plata and surroundings (aprox. 740.000 inh.), considered one of the six most affected cities by air pollution in the country. Since there is no monitoring network in the area, the obtained results should be considered as medium term accumulated data that enables to determine trends by analyzing together gas concentrations and meteorological parameters. Preliminary characterization of the behaviour of the predominant winds of the region in relation with potential atmospheric gas pollutants from seasonal wind roses is possible to carry out from the data. These results are complemented with monthly averaged SO(2) measurements. In particular, for year 2000, pollutant roses were determined which enable predictions about contamination emission sources. As a general result we can state that there is a clear increase in annual SO(2) concentration and that the selected site should be considered as a key site for future survey monitoring network deployment. Annual SO(2) average concentration and prevailing seasonal winds determined in this work, together with the potential health impact of SO(2) reveals the need for a comprehensive and systematic study involving particulate matter an other basic pollutant gases.     

Determination of O3, NO2, SO2, CO and PM10 measured in Belgrade urban area

O(3), NO(2), SO(2), CO and PM(10) concentrations, simultaneously determined for the first time in Belgrade urban area in the autumnal period of 2005, are presented. The obtained results display similar behaviour of SO(2), NO(2), CO, PM(10) opposite from that of O(3). The weekend effect was also investigated showing diminution of average daily concentrations of SO(2), NO(2), PM(10) and CO for 72, 40, 37 and 42% respectively, and increase of the average daily concentration of O(3) for 56%. Influence of meteorological conditions on observed concentration levels was studied, too. The observed influence of wind speed on the O(3) nightly concentration levels was analyzed pointing to the phenomena of O(3) transport during episodic measurements. To make an identification of possible pollution sources and analyse the influence of meteorological parameters on pollution levels, air back trajectories for high level concentrations episodes were calculated and analysed. A multivariate receptor modelling (Principal Component Analysis, Cluster Analysis) has been applied to a set of data in order to determine the contribution of different sources. It was found that the main principal components, extracted from the air pollution data, were related to gasoline combustion, oil combustion and ozone transport.     

Indoor air pollution levels in public buildings in Thailand and exposure assessment

Levels of pollutants including PM2.5 and PM2.5 composition (black carbon and water soluble ions), SO(2), NO(2), CO, CO(2), and BTEX (benzene, toluene, ethylbenzene, xylene) were monitored for indoor and outdoor air at a university campus and a shopping center, both located in the Northern suburb of Bangkok. Sampling was done during December 2005-February 2006 on both weekdays and weekends. At the university, indoor monitoring was done in two different air conditioned classrooms which shows the I/O ratios for all pollutants to be below 0.5-0.8 during the weekends. However, on weekdays the ratios for CO(2) and most detected BTEX were above 1.0. The concept of classroom occupancy was defined using a function of the student number in a lecture hour and the number of lecture hours per day. Classroom 2, which had a higher occupancy than classroom 1, was characterized by higher concentrations of most pollutants. PM2.5 was an exception and was higher in classroom 1 (37 microg/m(3), weekdays) as compared to classroom 2 (26 microg/m(3), weekdays) which was likely linked to the dust resuspension from the carpeted floor in the former. Monitoring was also done in the shopping mall at three different sites. Indoor pollutants levels and the I/O ratios at the shopping mall were higher than at the university. Levels of all pollutants measured at the car park, except for toluene and CO(2), were the highest. I/O ratios of the pollutants at the mall were above 1.0, which indicates the relatively higher influence of the indoor sources. However, the black carbon content in PM2.5 outdoor is higher than indoor, which suggest the important contribution from outdoor combustion sources such as the traffic. Major sources of outdoor air pollution in the areas were briefly discussed. Exposure modeling was applied using the time activity and measured pollutant concentrations to assess the exposure of different groups of people in the study areas. High exposure to PM2.5, especially for the people working in the mall, should be of health effect concern.     

睢宁地区大气PM_(2.5)及气态污染物季节性变化特征

根据2014年全年实时在线观测数据,分析了徐州睢宁地区大气细颗粒物(PM_(2.5))和气态污染物(包括SO_2、CO、NO_x、O_3)质量浓度的季节性变化特征。结合后向轨迹模型,分析不同气团对该地区大气污染浓度的影响。PM_(2.5)与O_3值在夏季最低,呈显著相关,表明夏季PM_(2.5)主要受控于本地大气光化学活性。在冬季,除O_3外,PM_(2.5)、SO_2、CO、NO_x值最高,且大气颗粒物主要以细粒子为主。O_3在春季最高,并与远程传输的气团且经过我国东部污染源密集地区相对应。高浓度的PM_(2.5)主要与冬季缓慢移动的气团相对应,这可能将PM_(2.5)及其气态前体物传输至该地区进而加重大气污染程度。     

Preparation and Validation of Gridded Emission Inventory of Criteria Air Pollutants and Identification of Emission Hotspots for Megacity Delhi

Delhi is one of the many megacities struggling with punishing levels of pollution from industrial, residential, and transportation sources. Over the years, pollution abatement in Delhi has become an important constituent of state policies. In the past one decade a lot of policies and regulations have been implemented which have had a noticeable effect on pollution levels. In this context, air quality models provide a powerful tool to study the impact of development plans on the expected air pollution levels and thus aid the regulating and planning authorities in decision-making process. In air quality modeling, emissions in the modeling domain at regular interval are one of the most important inputs. From the annual emission data of over a decade (1990–2000), emission inventory is prepared for the megacity Delhi. Four criteria pollutants namely, CO, SO₂, PM, and NO _x are considered and a gridded emission inventory over Delhi has been prepared taking into account land use pattern, population density, traffic density, industrial areas, etc. A top down approach is used for this purpose. Emission isopleths are drawn and annual emission patterns are discussed mainly for the years 1990, 1996 and 2000. Primary and secondary areas of emission hotspots are identified and emission variations discussed during the study period. Validation of estimated values is desired from the available data. There is a direct relationship of pollution levels and emission strength in a given area. Hence, an attempt has been made to validate the emission inventory for all criteria pollutants by analyzing emissions in various sampling zones with the ambient pollution levels. For validation purpose, the geographical region encompassing the study area (Delhi) has been divided into seven emission zones as per the air quality monitoring stations using Voronoi polygon concept. Dispersion modeling is also used for continuous elevated sources to have the contributing emissions at the ground level to facilitate validation. A good correlation between emission estimates and concentration has been found. Correlation coefficient of 0.82, 0.77, 0.58 and 0.68 for CO, SO₂, PM and NO _x respectively shows a reasonably satisfactory performance of the present estimates.     

Air Emissions Assessment from Offshore Oil Activities in Sonda de Campeche, Mexico

Air emission data from offshore oil platforms, gas and oil processing installations and contribution of marine activities at the Sonda de Campeche, located at the Gulf of Mexico, were compiled and integrated to facilitate the study of long range transport of pollutants into the region. From this important region, roughly 76% of the total Mexican oil and gas production is obtained. It was estimated that the total air emissions of all contaminants are approximately 821,000 tons per year. Hydrocarbons are the largest pollutant emissions with 277,590 tons per year, generated during flaring activities, and SOx in second place with 185,907 tons per year. Marine and aviation activities contribute with less than 2% of total emissions. Mass of pollutants emitted per barrel of petroleum produced calculated in this work, are in the range reported by similar oil companies.     

On-road emission factors from light-duty vehicles measured in Hsuehshan Tunnel (12.9 km), the longest tunnel in Asia

This study is based on 82 days of continuous air-quality monitoring using two mobile monitoring vans provided by the Taiwan Environmental Protection Administration (TEPA). The purpose of the study is to reveal variations of air quality without forced ventilation from on-road emissions of light-duty vehicles (LDV) inside the Hsuehshan Tunnel. Since it was opened to traffic in June 2006, only LDV have been allowed to pass the Hsuehshan Tunnel, the longest tunnel in Taiwan and in Asia. The hourly concentrations of air pollutants at four monitoring sites inside the tunnel (pollutant: downslope entrance, downslope exit, upslope entrance, upslope exit) are as follows: (CO: 6.9 +/- 5.4, 13.6 +/- 8.7, 4.6 +/- 3.2, 14.6 +/- 8.0 ppm), (NO(X): 534 +/- 311, 1198 +/- 640, 460 +/- 242, 1704 +/- 692 ppb), (SO2: 7 +/- 5, 18 +/- 10, 9 +/- 4, 26 +/- 10 ppb), (NMHC: 1.9 +/- 2.5, 3.5 +/- 3.0, 0.8 +/- 0.9, 2.7 +/- 1.9 ppm), (PM10: 50 +/- 28, 62 +/- 29, 59 +/- 25, 85 +/- 30 microg m(-3)), and (PM(2.5): 31 +/- 18, 45 +/- 22, 35 +/- 18, 62 +/- 23 microg m(-3)). The emission factors (EFs) from LDV in this study (pollutant: downslope EF, upslope EF in mg veh(-1) km(-1)) is estimated respectively as follows: (CO: 909 +/- 469, 1468 +/- 631), (NO(X): 145 +/- 67, 331 +/- 166), (SO2: 3 +/- 2, 6 +/- 3), (NMHC: 96 +/- 65, 121 +/- 63), (PM10: 2 +/- 2, 5 +/- 4), and (PM(2.5): 2 +/- 2, 4 +/- 3). It shows that the upslope pollutant EFs are twice as big as those of the downslope. However, among these EFs, the NMHC EF is less affected by the road gradient. Likewise, its upslope EF is only 1.3 times as big as that of the downslope. In summary, the results are useful for examining the emissions from the local fleet of vehicles under the driving conditions present during the study and for observing the changes in fleet emission rates upon future replication of the study.     

Air Quality and Monitoring Strategy in the Helsinki Metropolitan Area, Finland

The Helsinki Metropolitan Area Council (YTV) is responsible for air quality monitoring in the Helsinki area. Air quality has been monitored periodically since the late 1950s. An automatic SO2 monitoring network was constructed in 1975 and TSP measurements were added in 1978. Since then the network has been expanded and currently five automatic multicomponent stations form the basis of the network monitoring SO2, NO, NO2, CO, PM10 and O3 concentrations. Manual TSP and PM10 measurements are also conducted. Mobile monitoring units are also being used as well as special measurement campaigns. The effects of air pollution on nature are studied in bioindicator monitoring. An air quality index is used in order to inform the public of the current air quality situation. Changes in air quality are reflected in monitoring strategy. SO2 concentrations have decreased in the past two decades. Annual averages in 1995 were at or below 5 µg/m3. Traffic is the major source for pollutants even though catalytic converters have lowered traffic emissions somewhat. The highest annual average NO2 concentration at an urban site was 49 µg/m3 in 1995, and there has been no clear change in NO2 levels. There has been a decreasing trend in CO concentrations. Maximum annual TSP and PM10 averages in 1995 were 92 and 32 µg/m3, respectively. The highest average lead concentration was 0.01 µg/m3. Elevated concentrations are experienced from time to time. During the spring daily TSP and PM10 concentrations can go up to around 300 and 150 µg/m3, respectively. This is caused by resuspension mainly due to street sanding. Also a major winter NO2 episode occurred in December 1995. The highest hourly NO2 concentrations reached 400 µg/m3.     

杭州市大气污染物排放清单及特征

以杭州市区为研究区域,通过调查整合多套污染源数据库及其他统计资料,研究文献报道及模型计算的各种污染源排放因子,获得杭州市区各行业PM10、PM2.5、SO2、NOx、CO、VOCs、NH3等污染物的排放量,建立了杭州市区2010年1 km×1 km大气污染物排放清单。结果表明,2010年杭州市区PM10、PM2.5、SO2、NOx、CO、VOCs和NH3的排放总量分别为7.96×104、4.02×104、7.23×104、8.98×104、73.90×104、39.56×104、3.32×104t。从排放源的行业分布来看,机动车尾气排放是杭州市区大气污染物最重要排放源之一,对PM10、PM2.5、NOx、CO和VOCs的贡献分别达到14.4%、27.1%、40.3%、21.4%、31.1%。道路扬尘、电厂锅炉、工业炉窑、植被、畜禽养殖对不同污染物分别有着重要贡献,道路扬尘对PM10和PM2.5的贡献分别为44.6%和20.0%、电厂锅炉对SO2和NOx的贡献分别为37.0%和25.7%、工业炉窑对CO的贡献为41.5%、植被排放对VOCs的贡献为27.1%、畜禽养殖对NH3的贡献为76.5%。从空间分布来看,萧山区和余杭区对SO2、NH3和植被排放BVOC的贡献要显著高于主城区;而主城区机动车对PM2.5、NOx和VOCs的贡献分别达到36.3%、56.0%和47.4%,较市区范围内显著增加,表明机动车尾气排放已成为杭州主城区大气污染最重要的来源之一。     

集合数值预报系统在上海市空气质量预测预报中的应用研究

介绍了上海市世博环境空气质量集合数值预报系统的框架,分析了各模式在上海市空气质量预报及排放源污染贡献测算中的应用情况,提出开发多种预报手段、实施集合预报是提高城市空气质量预报准确率的发展方向。     

2020年江苏省机动车排放污染分布特征及与大气质量耦合性研究

依据生态环境部2021年6月发布的《排放源统计调查产排污核算方法和系数手册》,结合本地实测数据,在对汽油车颗粒物(PM)排放系数进行测算的基础上,核算了2020年江苏省机动车PM、氮氧化物(NO_(X))、挥发性有机物(VOC_(S))的排放总量,分析了机动车排放污染分布特征及与大气质量的耦合关系。结果表明:2020年江苏省机动车PM、NO_(X)、VOC_(S)排放量分别为0.5×10^(4),3.71×10^(5),1.17×10^(5) t。从区域分布来看,苏州、南京、无锡3市的3项污染物排放总量及NO_(X)、VOC_(S)排放量均位列前3位,PM排放量位列前3位的是苏州、徐州、无锡。从车型、燃料类型和排放阶段来看,国Ⅳ及以下排放标准的汽油小型客车是机动车VOC_(S)排放控制的重点,国Ⅲ排放标准的重型柴油货车是机动车PM和NO_(X)排放控制的重点。分析区域机动车PM排放量与大气中PM_(2.5)来源解析结果的耦合关系,其间存在不同程度的正相关性,控制机动车污染对改善大气环境会产生积极成效,南京、徐州和盐城3市的成效会尤为明显。     

An evaluation of improvements in the air quality of Beijing arising from the use of new vehicle emission standards

An innovative approach of mean emission by vehicle type was used in this paper to assess the impact of new vehicle emission standards in Beijing, China during the period of 2000–2005. It was found that CO and NO_x emissions decreased by 48% and 23%, respectively, from Type O (before 2000) to Type I (year 2000) vehicles. The reductions from Type O to Type II (year 2002) vehicles were 85% and 73% for CO and NO_x, respectively. When all three types of vehicles (Types O, I and II) are combined, the annual per vehicle CO emissions decreased from 586 kg per vehicle per year in 2000 to 324 kg per vehicle per year in 2005, while that of NO_x decreased from 66.9 to 43.4 kg per vehicle per year, which was mainly resulted from the impact of stringent new vehicle emission standards implemented in years 2000 and 2002. However, the vehicle population increased by 70% during the same time period, which offset the impact of cleaner vehicles. Thus, the total vehicle emission decreased little for CO (885,000 tons in 2000, 837,000 tons in 2005) and even increased slightly for NO_x (101,000 and 112,000 tons in 2000 and 2005, respectively). The ambient concentrations of CO decreased significantly throughout 2000–2005, the same trend was not observed for NO₂. Correlation analysis (grey correlation and Pearson correlation) between the annual vehicle emissions and annual concentrations of CO, the annual NO_x emission and annual NO₂ concentration indicated that the implementation of new vehicle emission standards was associated with the abatement of ambient CO and NO₂ concentrations in Beijing.     

2015—2017年江苏省秋、冬季空气质量状况

采用2015—2017年秋、冬季江苏省环境空气质量监测数据,从空气质量优良(达标)率、首要污染物、主要污染物浓度分析空气质量现状及特点。结果表明,江苏省秋、冬季空气质量优良(达标)率在60%左右,其中沿海地区空气质量达标率最高(71.1%),西北地区达标率最差(52.2%)。污染日的首要污染物主要为PM 2.5,占比高达91.5%。ρ(PM2.5)/ρ(PM 10)存在地区差异,江苏西北地区扬尘源贡献较大,江苏南部地区的二次颗粒物贡献更明显。ρ(NO2)/ρ(SO2)逐年持续升高,表明大气污染类型从燃煤性污染转变为复合型污染。     

Spatial variability of air pollutants in the city of Taranto, Italy and its potential impact on exposure assessment

Epidemiological studies typically use monitored air pollution data from a single station or as averaged data from several stations to estimate population exposure. In industrialized urban areas, this approach may present critical issues due to the spatial complexities of air pollutants which are emitted by different sources. This study focused on the city of Taranto, which is one of the most highly industrialized cities in southern Italy. Epidemiological studies have revealed several critical situations in this area, in terms of mortality excess and short-term health effects of air pollution. The aims of this paper are to study the variability of air pollutants in the city of Taranto and to interpret the results in relation to the applicability of the data in assessing population exposure. Meteorological and pollution data (SO2, NO2, PM10), measured simultaneously and continuously during the period 2006–2010 in five air quality stations, were analyzed. Relative and absolute spatial concentration variations were investigated by means of statistical indexes. Results show significant differences among stations. The highest correlation between stations was observed for PM10 concentrations, while critical values were found for NO2. The worst values were observed for the SO2 series. The high values of 90th percentile of differences between pairs of monitoring sites for the three pollutants index suggest that mean concentrations differ by large amounts from site to site. The overall analysis supports the hypothesis that various parts of the city are differently affected by the different emission sources, depending on meteorological conditions. In particular, analysis revealed that the influence of the industrial site may be primarily identified with the series of SO2 data which exhibit higher mean concentration values and positive correlations with wind intensity when the monitoring station is downwind from the industrial site. Results suggest evaluating the population exposure to air pollutants in industrialized cities by taking into account the possible zones of influence of different emission sources. More research is needed to identify an indicator, which ought to be a synthesis of several pollutants, and take into account the meteorological variables.     

Air quality assessment of Estarreja, an urban industrialized area, in a coastal region of Portugal

Despite the increasing concern given to air quality in urban and industrial areas in recent years, particular emphasis on regulation, control, and reduction of air pollutant emissions is still necessary to fully characterize the chain emissions–air quality–exposure–dose–health effects, for specific sources. The Estarreja region was selected as a case study because it has one of the largest chemical industrial complexes in Portugal that has been recently expanded, together with a growing urban area with an interesting location in the Portuguese coastland and crossed by important road traffic and rail national networks. This work presents the first air quality assessment for the region concerning pollutant emissions and meteorological and air quality monitoring data analysis, over the period 2000–2009. This assessment also includes a detailed investigation and characterization of past air pollution episodes for the most problematic pollutants: ozone and PM10. The contribution of different emission sources and meteorological conditions to these episodes is investigated. The stagnant meteorological conditions associated with local emissions, namely industrial activity and road traffic, are the major contributors to the air quality degradation over the study region. A set of measures to improve air quality—regarding ozone and PM10 levels—is proposed as an air quality management strategy for the study region.     

A Statistical Analysis of Particulate Data Sets for Jawaharlal Nehru Port and Surrounding Harbour Region in India

The concentrations of total suspended particulate matter (TSP) and particulate matter less than 10 microns (PM10) were measured at various locations in a Jawaharlal Nehru port and surrounding harbour region. Meteorological data was also collected to establish the correlation with air pollutant concentration. The results are analysed from the standpoint of monthly and seasonal variations, annual trends as well as meteorological effects. The monthly mean concentration of TSP was in the range of 88.2 to 199.3 microg m(-3). The maximum and minimum-recorded value of PM10 was 135.8 and 20.3 microg m(-3), respectively. The annual average concentration of PM10 was 66.1 microg m(-3). There are clear associations between TSP and PM10 data set at all the measured three sites with a correlation coefficient of 0.89, 0.69 and 0.81, respectively. PM10 data appears to be a constant fraction of the TSP data throughout the year, indicating common influences of meteorology and sources. Particle size analysis showed PM10 to be 47% of the total TSP concentration, which is lower than reported for industrial area and traffic junctions in Mumbai. Anthropogenic sources contribute significantly to the PM10 fraction in an industrial region, while contributions from natural sources are more in a port and harbour area. Statistical analysis of air quality data shows that TSP is strongly correlated with wind speed but weakly correlated with temperature. There appears to be a simple inverse relationship between TSP and wind speed data, indicating the dilution and transport by winds.