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1.
北京市主要PM10排放源成分谱分析   总被引:8,自引:0,他引:8  
对北京市土壤尘、道路扬尘、城市扬尘、建筑施工尘、钢铁尘、煤烟尘等主要PM10无组织排放源和固定源进行采样、分析,建立相应的成分谱数据库,通过对其化学组分分析,确定各类PM10排放源的化学组分特征和标识元素。土壤尘、建筑施工扬尘、钢铁尘、煤烟尘PM10的标识元素分别为Si、Ca、Fe、Al,道路扬尘显示出明显的土壤尘、建筑施工尘和机动车污染的特征,城市扬尘成分谱与道路尘有很强的共线性,具有明显的道路扬尘特征。  相似文献   

2.
南京市建筑扬尘排放清单研究   总被引:3,自引:0,他引:3  
统计分析了2010年南京市各行政区建筑场地面积和工期,结合扬尘排放因子,建立了南京市建筑扬尘排放清单。研究表明,2010年南京市建筑扬尘TSP、PM10和PM2.5的排放量分别达2.53万t、1.40万t和0.95万t,占工业烟(粉)尘排放量的23%、13%和8.6%。郊区县建筑扬尘排放量较大,约占全市 TSP、PM10、PM2.5排放总量的72%;主城区排放强度较高。对不同建筑工程类型扬尘排放量估算表明,城市建设工程和市政工程是建筑扬尘的主要来源,城市建设工程中又以住宅类建设工程为主。对不同研究获得的建筑扬尘结果比较,发现扬尘排放因子选择和污染源活动水平统计是影响建筑扬尘结果的关键因素。  相似文献   

3.
随着我国城市化的迅速发展,大气污染问题成为影响人们生活幸福感的重要因素之一。施工、裸露地面等引起的扬尘成为城市大气颗粒物污染的主要来源。将GIS方法和原环保部《扬尘源颗粒物排放清单编制技术指南(试行)》推荐的计算方法相结合,估算出2016—2018年长沙市城区施工扬尘源、土壤扬尘源的颗粒物排放量,从时空分布特征与空间自相关等多个方面剖析了扬尘源的颗粒物排放情况。结果表明:长沙市扬尘源沿中心城区向西、北方向辐射,呈现出半圆环形的带状分布,以城乡接合部最为集中;2016—2018年,长沙市城区扬尘源排放量总体呈现逐年递减的变化趋势。研究结果与长沙市城区实际情况基本相符,可为长沙市扬尘污染控制策略的制定提供参考。  相似文献   

4.
北京市主要水污染物排放特征及水质改善对策   总被引:3,自引:1,他引:2  
污染排放信息是环境决策的重要依据。分析了北京市水环境质量的现状,基于最新源排放清单,解析北京市当前主控污染物COD、氨氮排放的结构特征和空间特征,以期为北京市开展基于流域综合治理的水污染控制和水环境管理提供依据。按照工业源、农业源、生活源和集中处理设施的环境统计口径,2013年,COD、氨氮的排放构成分别为2.7%、37.1%、35.0%、25.3%和1.5%、20.1%、54.8%、23.6%。其中,农业源中畜禽养殖排放是主要来源,COD、氨氮总排放分别占农业源总排放量的94.7%和87.0%。在北京市五大水系中,北运河流域排放量最大,COD、氨氮排放量分别占全市总排放量的53.3%和57.4%。为改善北京市水环境质量,建议从加快污水处理厂提标改造、推动面源污染治理、加强水利联通、合理规划城市规模布局等4个方面入手。  相似文献   

5.
通过高斯面源反演的计算方法对天津市扬尘污染源进行反演计算,建立开放源可吸入颗粒物污染源强数据库,系统分析了城市扬尘污染问题。数值试验模拟结果表明,扬尘控制措施与环境质量呈现很好的线性相关关系,通过模拟2004年天津市建筑施工扬尘对城市可吸入颗粒物污染贡献,提出扬尘污染问题解决方案。  相似文献   

6.
应用卫星遥感影像结合无人机现场核查数据,对2020年江苏省各设区市主城区工地和裸地2类扬尘源的时空分布变化和污染、管控情况开展了连续性监测,为生态环境监测部门业务化应用提供了思路和方法。研究结果显示,遥感解译精度优于95%,扬尘源数量、面积均呈上升趋势,至第4季度总数达1760个、总面积162.53 km2,总体管控情况较好,全省防尘到位率为82%。在此基础上,应用排放因子法对全省及各市总悬浮颗粒物(TSP)、可吸入颗粒物(PM10)、细颗粒物(PM2.5)扬尘排放量进行了估算,并从扬尘减排的角度定量化评估了地方管控成效,全省全年TSP、PM10和PM2.5扬尘排放量分别为4.42×105,2.33×105,1.28×105 t,与完全无管控的理论最大排放值相比,TSP有效减排量为2.89×105 t,减少了65.4%。  相似文献   

7.
基于全省72个国控点位、185个网格化监测点位数据,利用GIS空间分析技术开展夏秋季江苏省大气臭氧与PM2.5污染热点分析。结果表明:江苏省夏秋季臭氧和PM2.5高值热点重合区域主要分布于徐州、宿迁,镇江、扬州、泰州、南通交界,以及苏州南部一带。结合该省污染源排放清单,在热点重合区域污染来源中,VOCs单位面积年均排放量居前三的为工业源[219.84 t/(km2·a)]、溶剂使用[87.13 t/(km2·a)]、居民生活源[47.77 t/(km2·a)],PM2.5污染行业单位面积排放量前三名为发电厂[129.79 t/(km2·a)]、工业源[39.67 t/(km2·a)]、扬尘源[29.20 t/(km2·a)];工业源与溶剂使用贡献73%的VOCs排放,工业源与发电厂贡献77%的PM2.5排放。为了有效推进江苏省臭氧与PM2.5的协同防控,须着重加强工业源、溶剂使用、发电厂、居民生活和扬尘的管控力度。  相似文献   

8.
裸地是扬尘的重要来源,施工建设过程中形成的裸地极易在大风天气作用下造成扬尘污染。因此,快速、有效地定位裸地位置,并确认其管控措施落实情况,对于开展裸地扬尘源监管具有重要意义。基于高分辨率遥感监测数据,结合人工解译裸地扬尘源数据集,以北京市大兴区为例,利用深度学习方法对裸地和防尘网覆盖裸地进行分类识别。同时,利用颜色匹配法对大兴区防尘网覆盖裸地进行识别,横向评估深度学习方法的识别精度。结果显示:深度学习方法对防尘网覆盖裸地的识别精度达97%,对裸地的识别精度达61%;颜色匹配法对防尘网覆盖裸地的识别精度达85%。防尘网覆盖裸地的颜色特征鲜明,深度学习方法和颜色匹配法对防尘网覆盖裸地的识别精度都在85%以上。深度学习方法对于面积大于2 000 m2的图斑有着较好的识别精度。深度学习方法可以提高裸地遥感解译的效率,实现规范化图像识别,可以作为人工判读的辅助手段。在实际应用中,可通过进一步积累样本来增强模型性能。深度学习方法适用于裸地扬尘源线索快速发现、工地防尘网措施落实情况快速检测等场景。  相似文献   

9.
基于洛阳市不同类型氨排放源的活动水平数据,主要采取排放因子法构建了2017年洛阳市大气氨排放清单,并以GIS技术为基础进行2 km分辨率的空间网格分配。通过研究得出,2017年洛阳市的大气氨排放量为63.2 kt,排放强度达到4 t/km~2以上,全市主要的氨排放源为畜禽养殖和农田生态系统,排放量分别为43.7 kt和10.4 kt,分别占氨排放总量的69.2%和16.5%。在畜禽养殖源中,肉牛是最大的贡献源,贡献率为30.4%;在农田生态系统中,氮肥施用是最大的贡献源,贡献率为87.7%。各区县中,宜阳县和伊川县排放量最大,共占氨排放总量的32.0%;偃师市、伊川县为排放强度最高;空间分布特征上呈现北部氨排放量大、南部排放量少、在城市区周边氨排放量较突出的现象。  相似文献   

10.
通过对浙江省统一开展部署和行动,现场调查收集全省7 507个施工工地、3 923个堆场以及不同等级公路和城市道路的真实活动水平数据,并基于点源地理信息和路网信息图层,采用排放系数法和ArcGIS工具构建了浙江省2015年3 km×3 km高空间分辨率扬尘源排放清单。结果表明,2015年浙江省扬尘源PM10和PM2.5的排放量分别为24.26×104 t和6.00×104 t,其中PM10和PM2.5排放贡献均主要为施工扬尘和道路扬尘,施工扬尘分别贡献37.7%和39.3%,道路扬尘分别贡献36.5%和39.1%。从城市空间分布来看,杭州市、宁波市、温州市、绍兴市扬尘排放总量居于全省前四,舟山市最低,而城市主城区排放量显著高于郊区。  相似文献   

11.
杭州市大气污染物排放清单及特征   总被引:15,自引:9,他引:6  
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。  相似文献   

12.
The relationship between indoor and outdoor particulate air pollution was investigated at an urban background site on the Payambar Azam Campus of Mazandaran University of Medical Sciences in Sari, Northern Iran. The concentration of particulate matter sized with a diameter less than 1 μm (PM1.0), 2.5 μm (PM2.5), and 10 μm (PM10) was evaluated at 5 outdoor and 12 indoor locations. Indoor sites included classrooms, corridors, and office sites in four university buildings. Outdoor PM concentrations were characterized at five locations around the university campus. Indoor and outdoor PM measurements (1-min resolution) were conducted in parallel during weekday mornings and afternoons. No difference found between indoor PM10 (50.1 ± 32.1 μg/m3) and outdoor PM10 concentrations (46.5 ± 26.0 μg/m3), indoor PM2.5 (22.6 ± 17.4 μg/m3) and outdoor PM2.5 concentration (22.2 ± 15.4 μg/m3), or indoor PM1.0 (14.5 ± 13.4 μg/m3) and outdoor mean PM1.0 concentrations (14.2 ± 12.3 μg/m3). Despite these similar concentrations, no correlations were found between outdoor and indoor PM levels. The present findings are not only of importance for the potential health effects of particulate air pollution on people who spend their daytime over a period of several hours in closed and confined spaces located at a university campus but also can inform regulatory about the improvement of indoor air quality, especially in developing countries.  相似文献   

13.
Elevated particulate matter concentrations in urbanlocations have normally been associated with local trafficemissions. Recently it has been suggested that suchepisodes are influenced to a high degree by PM10sources external to urban areas. To further corroboratethis hypothesis, linear regression was sought betweenPM10 concentrations measured at eight urban sites inthe U.K., with particulate sulphate concentration measuredat two rural sites, for the years 1993–1997. Analysis ofthe slopes, intercepts and correlation coefficientsindicate a possible relationship between urban PM10and rural sulphate concentrations. The influences of winddirection and of the distance of the urban from the ruralsites on the values of the three statistical parametersare also explored. The value of linear regression as ananalysis tool in such cases is discussed and it is shownthat an analysis of the sign of the rate of change of theurban PM10 and rural sulphate concentrations providesa more realistic method of correlation. The resultsindicate a major influence on urban PM10 concentrations from the eastern side of the UnitedKingdom. Linear correlation was also sought using PM10 data from nine urban sites in London and nearby ruralRochester. Analysis of the magnitude of the gradients andintercepts together with episode correlation analysisbetween the two sites showed the effect of transportedPM10 on the local London concentrations. This articlealso presents methods to estimate the influence of ruraland urban PM10 sources on urban PM10 concentrations and to obtain a rough estimate of thetransboundary contribution to urban air pollution from thePM10 concentration data of the urban site.  相似文献   

14.
In this study, the relationship between inhalable particulate (PM10), fine particulate (PM2.5), coarse particles (PM2.5 – 10) and meteorological parameters such as temperature, relative humidity, solar radiation, wind speed were statistically analyzed and modelled for urban area of Kolkata during winter months of 2003–2004. Ambient air quality was monitored with a sampling frequency of twenty-four hours at three monitoring sites located near traffic intersections and in an industrial area. The monitoring sites were located 3–5 m above ground near highly trafficked and congested areas. The 24 h average PM10 and PM2.5 samples were collected using Thermo-Andersen high volume samplers and exposed filter papers were extracted and analysed for benzene soluble organic fraction. The ratios between PM2.5 and PM10 were found to be in the range of 0.6 to 0.92 and the highest ratio was found in the most polluted urban site. Statistical analysis has shown a strong positive correlation between PM10 and PM2.5 and inverse correlation was observed between particulate matter (PM10 and PM2.5) and wind speed. Statistical analysis of air quality data shows that PM10 and PM2.5 are showing poor correlation with temperature, relative humidity and solar radiation. Regression equations for PM10 and PM2.5 and meteorological parameters were developed. The organic fraction of particulate matter soluble in benzene is an indication of poly aromatic hydrocarbon (PAH) concentration present in particulate matter. The relationship between the benzene soluble organic fraction (BSOF) of inhalable particulate (PM10) and fine particulate (PM2.5) were analysed for urban area of Kolkata. Significant positive correlation was observed between benzene soluble organic fraction of PM10 (BSM10) and benzene soluble organic fraction of PM2.5 (BSM2.5). Regression equations for BSM10 and BSM2.5 were developed.  相似文献   

15.
The aim of this study is to investigate the air pollution situation in an urban area in southwestern Luxembourg and to simulate annual NO2 and PM10 concentrations in response to changes in meteorological conditions and emissions using a Gaussian dispersion model. Simulations are carried out for the years 1998–2006. Emission scenarios related to road transport and nonindustrial combustion are performed in order to predict changes of air pollution levels. Road transport is by far the most important local emission source in the study area. Scenarios with more stringent emission standards for vehicles, less traffic, and fewer heavy-duty vehicles lead to reductions of NOx and primary PM10 emissions. As a result, the annual NO2 concentrations are decreasing in most parts of the study area and are below the European annual limit value of 40 μg?m?3. In contrast, a scenario with increased use of wood pellets for domestic heating shows an increase in urban PM10 concentration. The year-to-year variability of meteorological conditions accounts for the same magnitude of absolute NO2 and PM10 concentration changes as the emission scenarios. The comparison with measurements located in the study area shows that the model is able to predict urban-scale annual average air pollution. The proposed application results show that the model can be appropriate for policy-driven air quality management and planning queries.  相似文献   

16.
Studies conducted over the past decades have provided substantial evidence that both the long- and the short-term exposures to ozone and particulate matter are responsible for mortality and cardiopulmonary morbidity. This paper examines the relationship between exposure to ambient concentrations of ozone (O3) and particulate matter with aerodynamic diameter of less than 10 μm (PM10) and public health and provides the quantification of the burden of disease from PM10 and O3-related mortality and morbidity through a Life Cycle Impact Assessment focused on the greater area of Athens, Greece. Thus, characterizations factors (CFs) for human health damage are calculated in 17 sites in Athens, in terms of the annual marginal change in the disability-adjusted life years (DALYs) due to a marginal increase in the ambient concentrations. It is found that the PM10 intake factors range between 1.25?×?10?6 and 2.78?×?10?6, suggesting that 1.25–2.78 μg of PM10 are inhaled by the Athenian population per kg of PM10 in the urban atmosphere. Mortality due to chronic exposure to PM10 has a dominant contribution to years of life lost with values ranging between 6.2?×?10?5 and 1.1?×?10?4. On the other hand, the mortality caused by short-term exposure to O3 is weaker with the CFs ranging between 1.58?×?10?7?years of life lost in the urban/traffic areas and 4.71?×?10?7?years in the suburbs. Finally, it is found that 9,000 DALYs are lost on average in Athens, corresponding to 0.0018 DALYs per person. This is equal to 0.135 DALYs per person over a lifetime of approximately 75 years, assuming constant emission rates for the whole period.  相似文献   

17.
The objective of the study is to investigate seasonal and spatial variations of PM10 (particulate matter with aerodynamic diameter less than or equal to 10 μm) and TSP (total suspended particulate matter) of an Indian Metropolis with high pollution and population density from November 2003 to November 2004. Ambient concentration measurements of PM10 and TSP were carried out at two monitoring sites of an urban region of Kolkata. Monitoring sites have been selected based on the dominant activities of the area. Meteorological parameters such as wind speed, wind direction, rainfall, temperature and relative humidity were also collected simultaneously during the sampling period from Indian Meteorological Department, Kolkata. The 24 h average concentrations of PM10 and TSP were found in the range 68.2–280.6 μg/m3 and 139.3–580.3 μg/m3 for residential (Kasba) area, while 62.4–401.2 μg/m3 and 125.7–732.1 μg/m3 for industrial (Cossipore) area, respectively. Winter concentrations of particulate pollutants were higher than other seasons, irrespective of the monitoring sites. It indicates a longer residence time of particulates in the atmosphere during winter due to low winds and low mixing height. Spread of air pollution sources and non-uniform mixing conditions in an urban area often result in spatial variation of pollutant concentrations. The higher particulate pollution at industrial area may be attributed due to resuspension of road dust, soil dust, automobile traffic and nearby industrial emissions. Particle size analysis result shows that PM10 is about 52% of TSP at residential area and 54% at industrial area.  相似文献   

18.
大气污染物排放清单是了解大气污染特征和控制对策的前提。根据排放因子方法,建立了2018年西宁市金属(包括黑色和有色金属)冶炼和压延加工业PM2.5、PM10大气污染物的排放清单,并对其时空分布特征和清单不确定性进行了分析。结果表明:西宁市黑色金属冶炼和压延加工业PM2.5、PM10的总排放量分别是4.88×103、8.37×103 t;该行业对PM2.5、PM10排放量贡献率最大的是城北区,分别为58.36%、49.61%。有色金属冶炼和压延加工业PM2.5、PM10的总排放量分别是1.85×103、2.78×103 t,该行业对PM2.5、PM10贡献率最大的是大通县,分别为53.51%、56.99%。黑色金属冶炼和压延加工业对PM2.5、PM10贡献率最大的产业是粗钢产业,贡献率分别是38.41%、30.28%。有色金属冶炼和压延加工业对PM2.5、PM10贡献率最大的是铝行业,贡献率分别是97.33%和98.01%。2个行业PM2.5和PM10的排放受月份影响较小,一天中09:00—18:00是排放高峰期。蒙特卡罗法模拟结果表明:黑色金属冶炼和压延加工业95%置信区间的不确定性较高,PM2.5和PM10的不确定性分别为-59.33%~58.55%和-47.51%~47.28%。  相似文献   

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