广安市大气污染源排放清单研究

基于全面的实地调研,获取了广安市2016年各典型污染源的活动水平数据,以城市大气污染物排放清单编制技术手册为指导,采用排放因子法,建立了广安市2016年大气污染源排放清单,并分析了主要污染源排放特征。结果表明,2016年广安市SO_2、NO_X、CO、PM_(10)、PM_(2.5)、VOCs、NH_3总排放量分别为31 706 t、28 084 t、115 874 t、56 415 t、19 710 t、24 774 t以及39 484 t。SO_2排放主要来自工业源;NO_X排放主要来自工业源和移动源;CO排放主要来自工业源、民用燃烧源及移动源;PM_(10)和PM_(2.5)排放来自工业源、扬尘源和露天秸秆焚烧;VOCs主要来自工业源、移动源以及溶剂使用源;NH_3主要来自农业排放。     

南充市移动源污染物排放清单特征分析

基于2014年南充市大气污染源排放清单调查,通过实地调研、现场测试与统计年鉴等获得活动水平数据,采用排放系数法估算建立排放清单。结果表明道路机动车保有量为877 197辆,摩托车、载客汽车、载货汽车占比分别为61.8%、29.9%、8.3%。道路移动源CO 39 631.2t,NO_X26 448t、VOCs 20 544t、HC 3 648t、PM101 777t、PM_(2.5)1 600t、SO2391.7t,主要污染物为CO、NO_X和VOCs。柴油重型载货汽车、柴油轻型载货汽车、柴油大型载客汽车是NO_X、SO2、PM10和PM_(2.5)主要排放源,普通摩托车、其他燃料小型载客汽车是CO、VOCs主要排放源。普通摩托车和汽油中型载货汽车是HC主要排放源。非道路移动源污染物总量NO_X2 322t,CO 1 173t,HC 657.2t、PM 467.7t、PM_(2.5)252.9t、VOCs 179.8t。农业机械对CO、PM_(2.5)、PM、THC排放贡献率高,分别为49.5%、50.2%、48.3%、30.0%;工程机械对NO_X、PM_(2.5)、PM、THC的贡献率高,分别为51.4%、40.3%、38.9%、39.3%;船舶对VOCS排放贡献为90.3%。顺庆、高坪、嘉陵的CO、NO_X、THC、PM排放贡献率较高,蓬安VOCS排放贡献率较高。     

北京市区冬季PM_(2.5)污染特征及来源分析

选取北京市区为采样点,于2016年1月进行PM_(2.5)采集,并分析了PM_(2.5)和水溶性组分的污染特征和来源。结果表明,采样期间北京市PM_(2.5)质量浓度平均为67.7μg/m~3,水溶性离子是PM_(2.5)的主要组分,其中SO_4~(2-)、NO_3~-和NH_4~+之和占总离子的79.1%;Ca~(2+)和Mg~(2+)分别占PM_(2.5)质量浓度的2.5%和0.9%,海盐气溶胶和K~+分别占PM_(2.5)的3.6%和1.6%。采样期间NO_3~-/SO_4~(2-)为1.1,表明NO_2和SO_2主要来自移动源的贡献。北京市区冬季PM_(2.5)主要来自二次污染源、扬尘、生物质燃烧和海盐气溶胶,贡献率分别为42.351%、21.164%、16.314%和5.436%。     

“十二五”南充市城区空气质量变化及污染成因浅析

"十二五"期间,南充市城区空气质量于2014年之后有所改善,2015年达标率为74.37%,同比上年上升7.61个百分点;城区酸雨污染状况不断改善,酸雨频率和酸度逐年下降。城区主要污染物为可吸入颗粒物(PM10)和细颗粒物(PM_(2.5)),根据污染源排放情况,结合2016年3~4月细颗粒物(PM_(2.5))源解析结果,PM_(2.5)的来源主要为机动车尾气、二次无机源、燃煤、工业工艺源、扬尘、生物质燃烧等,严格控制机动车尾气和VOCs排放应为今后首要工作任务。     

南充冬季大气PM2.5污染特征及来源分析

为探究南充市冬季大气PM_(2.5)污染特征,于2017年1月对南充市大气PM_(2.5)进行采样,分析水溶性离子、无机元素和碳质组分的组成、浓度水平和来源。结果表明,二次无机离子SO_4~(2-)、NO_3~-、NH_4~+是南充市冬季大气PM_(2.5)水溶性离子中的重要组成部分,占总离子的86.7%;NH_4~+与NO_3~-和SO_4~(2-)主要是以NH_4NO_3和(NH_4)_2SO_4形式存在,SOR和NOR平均值分别为0.51和0.23,SOR高于NOR,说明南充市冬季硫氧转化速度比氮氧转化速度快且二次离子污染较为严重;NO_3~-/SO_4~(2-)比值为1.11,表明移动源是南充冬季大气污染物的主要来源,并且南充市冬季大气PM_(2.5)偏酸性。OC、EC是大气PM_(2.5)重要组成部分,OC/EC比值大于2,SOC对OC的贡献率较大(65.3%),南充市冬季大气PM_(2.5)中OC主要来源于二次污染。OC、EC之间相关性较好(R=0.84),二者具有共同的来源。主成分分析(PCA)结果表明,南充市冬季PM_(2.5)的主要来源是汽车尾气、燃煤、二次污染、生物质燃烧、土壤及建筑扬尘。     

炼化企业无机前体物排放对大气细颗粒物的贡献

大气污染物SO_2、NO_X、NH_3是形成二次细颗粒物(PM_(2.5))的重要无机前体物。为控制PM_(2.5)污染,要求形成一套便于炼化企业自身核算无机前体物排放对PM_(2.5)贡献的方法体系。探讨了基于排放清单、PM_(2.5)化学组成和NH_4~+/SO_4~(2-)摩尔浓度比值所建立的计算模型的合理性和可行性,并将其用于某炼厂进行案例分析。结果表明,所建立的计算模型可满足炼化企业PM_(2.5)核算要求,用于核算无机前体物排放对PM_(2.5)最大贡献量。案例企业排放前体物转化形成的PM_(2.5)等效排放量远高于该企业PM_(2.5)直接排放量,应予以关注。     

京津冀地区重点耗煤行业大气污染物排放清单研究

本研究通过京津冀地区各行业的年度煤炭消费量确定火电行业、钢铁行业和焦化行业为重点耗煤行业,以在线监测数据、污染源调查(现场调研、环评、验收)数据、排放因子数据为基础,自下而上建立了2013年京津冀地区重点耗煤行业大气污染物排放清单,分析研究了SO_2、NO_x和PM_(10)的排放量与污染贡献分布情况,掌握了京津冀地区重点耗煤行业大气污染物排放现状,为大气污染物减排提供数据基础。研究表明,2013年京津冀火电、钢铁焦化行业共排放SO_2 72.35万t、NO_x 131.99万t、PM_(10) 30.36万t。     

南昌市环境空气PM_(2.5)化学组分的时空分布特征

2015年在南昌市6个国控点分四个季度采集了大气PM_(2.5)样品,分析了其主要化学组分,并对PM_(2.5)质量浓度进行了重构。结果表明:南昌市PM_(2.5)的主要化学组分为SO_4~(2-)、OC、NO_3~-、NH_4~+和EC,占比具有明显的时空变化特征,硫酸盐在第二、三季度最大,硝酸盐在第一、四季度最大,SO_4~(2-)和NH_4~+在石化点位最高,NO_3~-在京东镇政府点位最高,OC和EC在省外办点位最高;重构后,南昌市PM_(2.5)以硫酸盐、有机物、地壳类物质为主,说明2015年南昌市扬尘和二次硫酸盐源类对PM_(2.5)的贡献可能是主要的。     

资阳市大气污染源排放清单研究

采用实地调研、资料收集等方式获得了2017年资阳市典型污染源的活动水平数据,参照城市大气污染物排放清单编制技术手册建立了基于排放因子法和物料衡算法的资阳市大气污染源排放清单,分析了主要污染物的行业排放特征和空间分布特征。结果表明,2017年资阳市SO₂、NO_X、CO、PM₁₀、PM_2.5、VOCs、NH₃总排放量分别为3.58kt、13.91kt、94.91kt、25.51kt、8.67kt、23.84kt和46.44kt。SO₂排放主要来自工业源;NOX排放主要来自移动源;CO排放主要来自工业过程及移动源;PM₁₀和PM_2.5、排放来自扬尘源和露天秸秆焚烧;VOCs主要来自溶剂使用源;NH₃主要来自农业活动。资阳市主要污染物排放分布在工业点源较为集中的雁江区和安岳县,乐至县污染物排放量相对较小。     

空气污染物的时空演化及社会经济驱动因素研究——以长江三角洲地区为例

本文以2006—2015年长江三角洲城市群为研究对象,分析该地区不同部门因能源消费而产生的典型污染物排放量,然后利用LMDI模型,对空气污染进行社会经济驱动因素分析。结果表明:该地区CO_2、SO_2、PM_(2.5)与PM_(10)等空气污染物排放量均呈现先快速增长后缓慢减少的趋势,排放的峰值多出现在了2013年,而NO_x则一直保持增长的趋势。其中,电力与工业部门是空气污染物的主要排放源,但对排放量贡献呈减少趋势,生活部门与交通部门污染物排放量则逐步增长,尤其是对PM_(2.5)与PM_(10)排放量的贡献不可忽视。人口与经济增长对污染物排放量起到了正向拉动作用,经济因素的驱动作用最为明显,其效应值呈现先小幅增加后大幅下降的趋势,能源效率与能源结构有抑制作用,其对污染物排放的效应值仅次于经济因素,而能源结构变化的效应很小。     

《大气污染防治行动计划》实施环境效果模拟

本研究利用2010年污染源普查数据和MEIC排放清单建立全国大气污染物高时空分辨率排放清单,在此基础上利用2012年环境统计数据对其进行修订建立2012年全国大气污染物高时空分辨率排放清单;结合《大气污染防治行动计划》(以下简称《计划》)研究工作,测算了《计划》实施后在污染源综合治理、落后产能淘汰、能源结构调整方面对SO2、NOx、颗粒物、VOCs的减排量,同时对污染物新增量进行了预测,建立了《计划》实施后全国大气污染物高时空分辨率排放清单;利用CMAQ空气质量模型模拟分析了《计划》实施的空气质量改善效果。结果表明:《计划》实施后,将可以减少641万吨SO2、859万吨NOx、547万吨颗粒物(不含扬尘污染控制)、627万吨VOCs,全国、京津冀、长三角及珠三角区域PM2.5年均浓度将分别比2012年下降22.08%、33.99%、23.98%、24.04%。如果《计划》要求全部落实,可以实现空气质量改善目标。     

Quantifying the sources of ozone, fine particulate matter, and regional haze in the Southeastern United States

A detailed sensitivity analysis was conducted to quantify the contributions of various emission sources to ozone (O₃), fine particulate matter (PM_2.5), and regional haze in the Southeastern United States. O₃ and particulate matter (PM) levels were estimated using the Community Multiscale Air Quality (CMAQ) modeling system and light extinction values were calculated from modeled PM concentrations. First, the base case was established using the emission projections for the year 2009. Then, in each model run, SO₂, primary carbon (PC), NH₃, NO_x or VOC emissions from a particular source category in a certain geographic area were reduced by 30% and the responses were determined by calculating the difference between the results of the reduced emission case and the base case.The sensitivity of summertime O₃ to VOC emissions is small in the Southeast and ground-level NO_x controls are generally more beneficial than elevated NO_x controls (per unit mass of emissions reduced). SO₂ emission reduction is the most beneficial control strategy in reducing summertime PM_2.5 levels and improving visibility in the Southeast and electric generating utilities are the single largest source of SO₂. Controlling PC emissions can be very effective locally, especially in winter. Reducing NH₃ emissions is an effective strategy to reduce wintertime ammonium nitrate (NO₃NH₄) levels and improve visibility; NO_x emissions reductions are not as effective. The results presented here will help the development of specific emission control strategies for future attainment of the National Ambient Air Quality Standards in the region.     

我国长江流域中三角区域大气污染物排放特征研究

中三角区域已经是我国第四个国家级城市群,也将成为我国经济增长的"第四极"。在经济发展的同时,更需要以节能减排、资源环境等为重点,以实现经济建设与生态文明"双可持续"的协同发展。本文以二氧化硫、氮氧化物、烟(粉)尘为主要大气污染物,对我国中三角区域大气污染物排放进行了详细的分析,并与京津冀、长三角、珠三角、"三区十群"等进行了多方位比较。结果表明,2013年中三角区域二氧化硫排放量为151.7万t,其中工业二氧化硫排放量为140.1万t;氮氧化物排放量为147.2万t,其中工业氮氧化物排放量为93.6万t;烟(粉)尘排放量为81.8万t,其中工业烟(粉)尘排放量为71.4万t。中三角区域二氧化硫、氮氧化物、烟(粉)尘排放量均位于"四极"的第三。中三角区域二氧化硫、氮氧化物、烟(粉)尘单位GDP排放强度分别为25.03t/亿元、24.29 t/亿元、13.50 t/亿元,分别位于"四极"的第一、第二、第二。同时,本文还从经济发展模式、产业结构调整、煤炭消费方式等方面对我国中三角等经济"四极"提出了相关建议。     

城市电动汽车规模化发展的大气环境影响研究

发展电动汽车被认为是有效缓解城市交通污染的重要措施,但大规模的电动汽车发展不仅有增加电力部门排放的风险,而且可能影响电网运营的稳定性。本研究以南京市为例,综合应用充电行为模式调研、蒙特卡洛模拟、COPERT模型、排放因子法等方法,系统研究了私家车、出租车和公交车三种类型电动汽车的充电特征及其对区域交通和电力部门排放的影响。结果表明,当三种车型的电动化率分别达到50%、100%和100%时,城市的NOx、PM_(2.5)、CO、VOCs和CO_2排放量将分别比基准情景减少378t、305t、20 223t、3649t和480万t。但是,SO_2排放增加了1152t,并且导致南京市电网的夏季峰值负荷增加10%。为更好地改善中国城市环境空气质量,应综合考虑电动汽车有序充电、协同促进清洁电力等发展策略,最大限度地实现电动汽车的环境效益。     

The impact of urban growth on regional air quality surrounding the Langat River Basin, Malaysia

Studies of air quality were carried out in the towns of Kajang, Nilai and Banting in the Langat River Basin, southern region of Kuala Lumpur to determine the status and trend of air quality. The determination of air quality was based on several parameters such as suspended solids with diameters less than 10???m (PM₁₀) and gaseous pollutants of sulphur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO). Primary concentration data of air pollutants were compiled through fieldwork studies and combined with secondary data obtained from the regular monitoring data as collected by Alam Sekitar Malaysia Sdn. Bhd. (ASMA) on behalf of Malaysian Department of Environment (DOE) at their stations in Kajang and Nilai. Results showed that the average concentrations of PM₁₀, SO₂, NO₂, O_3, and CO at all sampling stations were still below the permissible values recommended by the Malaysian DOE. The level of gaseous pollutants of NO₂, O_3, and CO was recorded at statistically higher levels (p?<?0.05) than values recorded at the control station at Pangsun Recreational Area. These pollutants were suspected to have originated mainly from exhaust systems of motor vehicles. Data for the years 1996 to 2006 as obtained from ASMA showed long-term air quality trends of increasing O₃ and NO₂ concentrations in Kajang whilst concentrations of PM₁₀ recorded at both Kajang and Nilai stations were mostly expected coming from transboundary sources especially biomass burning and the development activities around the study areas.     

Impact of COVID-19 lockdown on the elemental profile of PM10 present in the ambient aerosol of an educational institute in Kolkata,India

Reduction in air pollution level was prime observation during COVID-19 lockdown globally. Here, the study was conducted to assess the impact of lockdown on the elemental profile of PM₁₀ in ambient aerosol to quantify the elemental variation. To quantify the variation, phase-wise sampling of air pollutants was carried out using the gravimetric method for PM₁₀, while NO₂ and SO₂ were estimated through the chemiluminescence and fluorescent spectrometric method respectively. The elemental constituents of PM₁₀ were carried out using an Inductively Coupled Plasma Optical Emission Spectrometer and their source apportionment was carried out using the Positive Matrix Factorization model. The results showed that PM₁₀, NO₂ and SO₂ reduced by 86.97%, 83.38%, and 88.60% respectively during the lockdown sampling phase. The highest mean elemental concentration reduction was found in Mn (97.47%) during the lockdown. The inter-correlation among the pollutants exhibited a significant association indicating that they originate from the same source. The metals like Mn and Cu were found at a higher concentration during the lockdown phase corresponding to vehicular emissions. The comparative analysis of the elemental profile of PM₁₀ concluded that the lockdown effectuated in reduction of the majority of elements present in an aerosol enveloping metropolitan like Kolkata.     

柳州市2009-2014年空气污染变化

近年来,城市空气污染日益严重,已成为公众广泛关注的环境问题之一。柳州是中国西部的工业重镇、广西有名的工业城市,位列国家划定的113个大气污染防治重点城市之中,是广西第一个开展PM2.5监测的城市。本研究于2009—2014年连续6年对柳州市大气主要污染物SO2、NO2、PM10和PM2.5的浓度进行在线观测,获得了污染物的长期时间和空间分布特征。结果显示,SO2浓度呈逐年下降趋势,并于2011年达标之后显著下降,2014年相比2009年下降了50.0%;NO2浓度一直在低于标准以下波动(24.6~35.1μg/m3);PM10浓度呈逐年增长趋势,并从2011年开始超标,2014年相对于2009年增长了69.3%。各污染物浓度都具有显著的季节变化:冬季秋季春季夏季。SO2、NO2、PM10和PM2.5的浓度冬季相比夏季分别提高82.9%、56.3%、66.9%和133.6%。冬季SO2和秋冬季PM10超标,PM2.5除7月外全线超标。PM2.5/PM10的比值冬季也高于夏季,表明冬季更易富集细颗粒。各污染物浓度也表现出不同的空间分布。九中各污染物的浓度都最高,可能与其离柳州钢铁公司距离较近有关。SO2除九中外,其他站点均达标。NO2全部达标。PM10市监测站和九中超标。PM2.5所有站点超标严重。本研究结果表明,柳州市煤烟型污染得到有效控制,但颗粒物污染,尤其是细颗粒物污染日益严重。     

Temporal‐spatial variations of air pollutants in Lanzhou,Gansu Province,China, during the spring–summer periods, 2014–2016

In this article, we analyzed the mass concentrations of particulate matter 2.5 micrometers (µm) or less in size (PM_2.5), particulate matter 10 µm or less in size (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) in Lanzhou, the capital of Gansu province, China. We analyzed monitoring data collected from five air quality monitoring stations during the spring–summer period from 2014 to 2016. Our comparison of contaminant concentrations and average diurnal, daily, monthly, and annual concentrations revealed that the average concentrations of PM_2.5 and PM₁₀ amounted to 128.57 and 46.4 micrograms per cubic meters (µg/m³), respectively, exceeding the Chinese National Ambient Air Quality Standard (NAAQS). We used the Pearson correlation coefficient to establish connections between particulate matter and gaseous pollutants. The results show significant differences in the concentration levels of airborne pollutants. The Pearson correlation coefficient between PM_2.5 and PM₁₀ had the highest coefficient of r = 0.842. A correlation between the two particulate matter sizes (PM_2.5 and PM₁₀) and SO₂ was PM_2.5 and SO₂ r = 0.313; PM₁₀ and SO₂ r = 0.279; and CO and the two particulate matter sizes, PM_2.5 and CO r = 0.304; and PM₁₀ and CO r = 0.203. The average monthly ratio for the study months of PM_2.5 to PM₁₀ was 0.361. In addition, we used the hybrid single particle Lagrangian integrated trajectory model for tracking sources and pathways of the air pollutants in Lanzhou.     

Impacts on air pollution in urban areas

Environmental management involves controlling various forms of pollution to levels that do not pose a threat to the health of the people and the environment in general. This paper presents a framework to analyze sources of local air pollution in cities. Using an OLS model, an investigation is performed of the relationship among the concentrations of air pollutants [more precisely, concentrations of sulfur dioxide (SO₂), dust, nitric oxide (NO), nitrogen dioxide (NO₂), carbon oxide (CO) and ozone (O₃)], economic activities, and meteorology. Time series analysis leads to a model, that explains a high degree of the variance in the air pollution data. The model is applied to daily time series from three measurement stations in innsbruck, Austria. Estimation results of the model generally fit with the expected relations. Space heating influences SO₂, dust, and NO, while NO₂ levels are primarily affected by traffic. These results also indicate interdependent relations among the pollutants NO, NO₂, O₃, and CO; O₃ levels depend on temperature and sunshine.