四川省水泥工业大气污染物排放水平分析

通过调研2018年四川省37条水泥生产线活动水平数据,结合企业污染治理技术,分析该省水泥工业的主要大气污染物排放水平。结果表明:2018年四川省水泥行业SO₂、NO_x、PM_2.5和PM₁₀的排放量分别为1.2万t、5.5万t、3.9万t和6.5万t,其不确定性主要来自污染物的产生系数和去除效率。四川省水泥生产企业各工序排放的颗粒物、SO₂和NO_x浓度总体上均低于现行标准,部分工序颗粒物超标主要受布袋的去除效率影响。     

基于区域环境空气质量和碳排放数据的减污降碳协同效果评估

化石燃料燃烧产生的温室气体与大气污染物具有同根同源性，但具体治理中减污降碳的协同效果尚不明确。以浙江省11个设区市为研究样本，对环境空气质量和二氧化碳(CO₂)排放数据进行分析研究，结果显示：2016—2020年浙江省环境空气质量持续改善，但CO₂排放总量仍处于增长阶段。11个设区城市PM_2.5年均浓度降幅在26%～41%之间，二氧化氮(NO₂)年均浓度下降趋势不明显，大部分城市呈现碳排放增加、NO₂浓度下降的特征，只有杭州和温州两市呈现碳排放总量和NO₂、PM_2.5浓度协同下降的趋势。因子相关性分析结果表明，各设区市呈现NO₂浓度与碳排放相关性较大、协同性强，PM_2.5浓度与碳排放相关性较小的特点。进一步通过减污降碳协同定量评价分析表明，浙江地区在环境空气质量改善和温室气体减排已表现出一定成效，但各设区市因产业结构、环境基础条件、协同程度等不同导致减污降碳综合绩效有明显差异。从源头减排实现...     

临汾市大气污染物的时间分布特征与源区分析

多年来，临汾市多次名列我国生态环境部公布的空气质量最差的重点城市之列，对其大气污染的时间分布特征和潜在源区进行分析对其环境管理与污染防治具有重要意义。利用2015—2019年临汾市5个国控空气环境质量监测站点的6种空气污染物(SO₂、NO₂、CO、O₃、PM_2.5和PM₁₀)浓度数据和气象观测数据，使用HYSPLIT模型研究了该市空气污染物的时间变化特征、轨迹输送特征和可能的来源。结果表明，PM_2.5和PM₁₀的年均浓度均超过了《环境空气质量标准》(GB 3095—2012)Ⅱ级标准，SO₂仅在2016—2017年超过该标准，其余3种污染物的年均浓度均低于该标准。6种污染物2015—2019年的月均浓度的变化特征表现为O₃浓度呈以6、7月为中心的近似正态分布，SO₂、NO₂和CO以及PM_2.5和PM₁₀浓...     

2017年江苏省PM2.5数值模拟及内外源解析

根据江苏省72个国控点监测数据,采用了区域大气模式和多尺度空气质量模式系统(RAMS-CMAQ)模拟了2017年江苏省ρ(PM_2.5)的时空分布,耦合综合源追踪算法(ISAM)分析了不同地区排放源对ρ(PM_2.5)的贡献特征。结果表明,PM_2.5模拟与观测值的相关系数(r)=0.76,标准平均偏差(NMB)=5.2%,均方根误差(RMSE)=23.4μg/m₃,模拟结果落于观测结果0.5~2倍的比例(FAC2)=84.2%。源追踪模块结果显示,夏季主要受东南风控制,本地排放的贡献更大(省内贡献为52.34%),其他季节受偏北风输送影响,外源输送的影响较大(省外贡献为53.48%~56.84%);冬季苏北5市的排放贡献比沿江8市的更大,而春、夏季沿江8市排放贡献较大。     

基于KZ滤波法的江淮地区PM2.5浓度变化影响分析

基于2018—2020年合肥、芜湖和马鞍山3个城市国控站点的PM_2.5逐日监测数据和同期地面气象观测资料，利用Kolmogorov-Zurbenko(KZ)滤波对PM_2.5日浓度的原始时间序列进行分解，获取短期分量、季节分量和长期分量，并进行多元线性逐步回归构建各分量与气象因子的模型，最后依据短期分量和基线分量的回归模型和残差分析，对序列进行重建，获取消除气象条件影响的PM_2.5长期分量。KZ滤波分析结果表明：2018—2020年气象条件对江淮区域PM_2.5污染改善影响存在波动，在2018—2019年为负贡献，而在2020年秋冬季则变为正贡献；江淮地区3个城市2018年和2020年PM_2.5修正后的长期分量均值表明气象条件对各市PM_2.5改善影响存在差异较大，气象条件对合肥PM_2.5改善的贡献仅为1.0%,芜湖为7.8%,马鞍山为21.0%;NAQPMS数值模式情景分析结果显示，减排措施对江淮之间PM_2.5浓度改...     

合肥市冬季细颗粒物中碳组分特征分析

利用2020年12月1日至2021年2月28日合肥市细颗粒物(PM_2.5)、有机碳(OC)和元素碳(EC)等环境空气质量监测数据和气象观测数据，分析了合肥市大气PM_2.5中OC和EC的污染特征，并探讨了其来源以及气象因素影响。结果表明：合肥市冬季碳质气溶胶是PM_2.5中主要组分，随着污染程度的加重，碳质气溶胶的质量浓度逐步增加，但其在PM_2.5中的占比先减小后增加。在以PM_2.5为首要污染物的不同污染级别天气条件下，OC和EC的相关性说明不同程度下碳质气溶胶来源复杂。OC/EC表明机动车尾气和燃煤源排放是碳质气溶胶的主要来源。二次有机碳(SOC)会随着污染程度的加重而呈现升高趋势。OC和EC在冬季受温度影响较小；较大的相对湿度对OC和EC具有一定的清除作用，明显降水或连续降水的清除作用更加显著；而风速对含碳气溶胶的影响主要出现在污染天气背景下。     

苏州大气颗粒物PM2.5和PM10质量浓度异常“倒挂”现象的成因及影响分析

为深入研究PM_2.5和PM₁₀质量浓度异常“倒挂”现象的成因及影响，在苏州市相城区国控点开展比对监测分析，回顾性分析了2016—2020年苏州全部国控点颗粒物浓度数据。苏州市相城区国控点PM_2.5浓度的比对分析结果表明：该国控点频繁出现PM_2.5浓度高于其他国控点PM_2.5浓度和高于该站点PM₁₀浓度(“倒挂”率高达34%)的“双高”现象，PM_2.5平均浓度比其他9个国控点高12.5%～37.2%,比位于同一站点的备用监测仪器(“倒挂”率为0)高38.1%。2016—2020年，苏州全部国控点“倒挂”时间的总体趋势都是逐年递增，且集中发生在相对湿度较高的20:00至次日07:00。这5年间各国控点PM_2.5浓度异常偏高导致的异常“倒挂”现象对全市年均浓度产生的正误差分别为1.6%、2.8%、6.0%、6.2%和4.1%,基本呈现出逐年递增的趋势。上述结果表明：苏州PM_2.5浓度偏高是由动态加...     

“十三五”期间伊宁市大气污染特征分析及对策建议

基于伊宁市“十三五”期间大气国控监测点位数据，分析伊宁市“十三五”期间环境空气质量变化特征并提出建议对策。结果表明：“十三五”期间，伊宁市空气优良率在78.9%～86.3%,重污染天气在3～17 d,重污染天气仍频发；PM_2.5年均浓度在38～47μg/m³,年均值均超标。影响空气质量的主要污染物为PM_2.5、PM₁₀和CO。与三大区域相比，伊宁市SO₂和CO污染程度相对较重，燃煤型的污染特征显著。此外伊宁市采暖季空气污染较重，PM_2.5、SO₂、CO等污染物浓度显著高于非采暖季，采暖季主要污染物呈现双峰变化特征。     

台州市区PM2.5和O3污染特征及相互作用关系

基于2016—2020年台州市区大气污染物监测数据及气象观测资料，分析了台州市区PM_2.5和O₃的污染特征及受气象因素影响情况，并探究了不同季节下的PM_2.5浓度和O₃浓度的相关性及相互作用关系。2016—2020年，台州市区PM_2.5年均浓度和超标天数呈显著下降趋势，O₃-8 h年均浓度和超标天数总体呈上升趋势。PM_2.5浓度在冬季最高，且易发生超标；O₃浓度在春、夏、秋季均较高，且均会发生超标。通过相关性分析可知：PM_2.5浓度与气温、相对湿度、风速、降水量呈负相关，与大气压呈正相关；O₃浓度与气温、风速呈正相关，与相对湿度、降水量呈负相关。不同季节下的PM_2.5浓度与O₃浓度均呈正相关，两者存在协同增长。在春、夏、秋季，二次PM_2.5在总PM_2.5中的占比随着O₃     

烟花爆竹燃放对四川盆地边缘城市环境空气的影响及其污染传输特征研究

为了解四川盆地边缘城市环境空气质量受烟花爆竹燃放影响的特征，选取位于成都平原西南部的乐山市进行研究。研究发现，春节期间乐山市受烟花爆竹燃放影响的环境空气中的颗粒物及其组分的浓度变化趋势一致。相关性分析结果显示：在除夕夜烟花爆竹燃放影响时间段内，乐山市环境空气中的颗粒物(PM₁₀、PM_2.5)浓度与SO₄^2-、F^-、Al、K、Mn、Cu、Zn等组分浓度呈显著正相关，Pearson相关系数达到了0.48～0.73;Al、Si、K、Mn、Cu、Zn等组分浓度之间也表现出显著正相关，Pearson相关系数高达0.78～0.98。其中，K⁺浓度占比从0%上升到7.32%,SO₄^2-浓度占比从18.94%上升到24.73%,Cl^-浓度占比从0.46%上升到4.29%,Cu元素浓度占比从0.05%上升到0.30%,Al元素浓度占比从5.87%上升到7.46%。除夕夜烟花爆竹燃放影响期间，乐山市中部和北部区...     

秸秆焚烧对空气质量影响特征及判别方法的研究

利用南京空气自动监测数据及PM_(2.5)组分监测结果,分析了2011年夏收秸秆焚烧期间大气污染特征,并探寻快速判别秸秆焚烧影响的指标及方法。结果表明:秸秆焚烧期间PM_(2.5)污染特征显著,其组分中K~+、EC、OC等浓度相对偏高。基于离子组分及碳元素在线监测数据,可选取K~+作为快速判别指标,并根据K~+与PM_(2.5)的相关性,计算秸秆焚烧对PM_(2.5)的贡献。同时结合OC、EC浓度变化,综合判别秸秆焚烧对空气质量的影响程度。     

Traffic emission factors of ultrafine particles: effects from ambient air

Ultrafine particles have a significant detrimental effect on both human health and climate. In order to abate this problem, it is necessary to identify the sources of ultrafine particles. A parameterisation method is presented for estimating the levels of traffic-emitted ultrafine particles in terms of variables describing the ambient conditions. The method is versatile and could easily be applied to similar datasets in other environments. The data used were collected during a four-week period in February 2005, in Gothenburg, as part of the G?te-2005 campaign. The specific variables tested were temperature (T), relative humidity (RH), carbon monoxide concentration (CO), and the concentration of particles up to 10 μm diameter (PM(10)); all indicators are of importance for aerosol processes such as coagulation and gas-particle partitioning. These variables were selected because of their direct effect on aerosol processes (T and RH) or as proxies for aerosol surface area (CO and PM(10)) and because of their availability in local monitoring programmes, increasing the usability of the parameterization. Emission factors are presented for 10-100 nm particles (ultrafine particles; EF(ufp)), for 10-40 nm particles (EF(10-40)), and for 40-100 nm particles (EF(40-100)). For EF(40-100) no effect of ambient conditions was found. The emission factor equations are calculated based on an emission factor for NO(x) of 1 g km(-1), thus the particle emission factors are easily expressed in units of particles per gram of NO(x) emitted. For 10-100 nm particles the emission factor is EF(ufp) = 1.8×10(15)×(1 - 0.095×CO - 3.2×10(-3)×T) particles km(-1). Alternative equations for the EFs in terms of T and PM(10) concentration are also presented.     

桂林市细颗粒物部分典型排放源的粒径谱及成分分析

采用在线单颗粒气溶胶质谱技术源解析方法,对桂林市PM2.5典型排放源的粒径和化学成分进行质谱分析,采集燃煤/燃气源、工业工艺源、扬尘源、油烟源4类共计7个典型排放源。结果表明,桂林市4类排放源细颗粒物的粒径分布为0.25~1.25μm,80%以上的细颗粒分布在0.2~1.0μm的小粒径范围,峰值约0.68μm。细颗粒物离子成分含有Na~+、Mg~+、K~+、NH~+4、Fe~+、Pb~+、Cd~+、V~+、Mn~+、Li~+、Al~+、Ca~+、Cu~+、Zn~+、Cr~+、CN~-、PO_3~-、NO_2~-、NO_3~-、Cl~-、SO_4~(2-)、SiO_3~-等成分,桂林市细颗粒物为元素碳、有机碳元素碳、有机碳、富锰颗粒、富铁颗粒、富钾颗粒、矿物质、左旋葡聚糖以及其他金属等9类。     

The Effect of Traction Sanding on Urban Suspended Particles in Finland

Springtime urban road dust forms one of the most serious problems regarding air pollution in Finland. The composition and origin of springtime dust was studied in southern Finland with two different methods. Suspended particles (PM10 and TSP) were collected with high volume particle samplers and particle deposition was collected with moss bags. The composition of the PM(1.5-10) fraction was studied using individual particle analysis with SEM/EDX. The deposition in the moss bags was analysed with ICP-MS. The results showed that during the study period, approximately 10% of both PM(1.5-10) particles and the deposition originated from sanding. Other sources in the springtime PM(1.5-10) were e.g. asphalt aggregate or soil and combustion processes. It can be concluded that sanding produced a relatively small amount of particulate matter under the investigated circumstances.     

Study of size and mass distribution of particulate matter due to crop residue burning with seasonal variation in rural area of Punjab, India

Emission from field burning of agricultural crop residue is a common environmental hazard observed in northern India. It has a significant potential health risk for the rural population due to respirable suspended particulate matter (RSPM). A study on eight stage size segregated mass distribution of RSPM was done for 2 wheat and 3 rice crop seasons. The study was undertaken at rural and agricultural sites of Patiala (India) where the RSPM levels remained close to the National Ambient Air quality standards (NAAQS). Fine particulate matter (PM(2.5)) contributed almost 55% to 64% of the RSPM, showing that, in general, the smaller particles dominated during the whole study period with more contribution during the rice crop as compared to that of wheat crop residue burning. Fine particulate matter content in the total RSPM increased with decrease in temperature. Concentration levels of PM(10) and PM(2.5) were higher during the winter months as compared to that in the summer months. Background concentration levels of PM(10), PM(2.5) and PM(10-2.5) were found to be around 97 ± 21, 57 ± 15 and 40 ± 6 μg m(-3), respectively. The levels increased up to 66, 78 and 71% during rice season and 51, 43 and 61% during wheat crop residue burning, respectively. Extensive statistical analysis of the data was done by using pair t-test. Overall results show that the concentration levels of different size particulate matter are greatly affected by agricultural crop residue burning but the total distribution of the particulate matter remains almost constant.     

乌鲁木齐市可吸入颗粒物水溶性离子特征及来源解析

采暖期时在乌鲁木齐市采集了环境空气中的可吸入颗粒物,对可吸入颗粒物质量浓度及8种水溶性离子的特征和来源进行了分析。结果表明,细粒子和粗粒子的月平均质量浓度分别是53.5～233.3μg/m3和38.9～60.9μg/m3;细粒子和粗粒子中水溶性离子主要由SO24-、NH4+和NO3-组成;粗粒子中NH4+与NO3-和SO24-的相关性分别是0.70和0.66,细粒子中NH4+与NO3-和SO24-的相关性分别是0.89和0.93,铵盐是乌鲁木齐可吸入颗粒物主要存在形式;煤烟尘是乌鲁木齐市采暖期可吸入颗粒物的主要来源。     

Risk assessment of heavy metals in road and soil dusts within PM2.5, PM10 and PM100 fractions in Dongying city, Shandong Province, China

15 road and 14 soil dust samples were collected from an oilfield city, Dongying, from 11/2009-4/2010 and analyzed by inductively coupled plasma-mass spectroscopy (ICP-MS) for V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd and Pb within PM(2.5), PM(10) and PM(100) fractions synchronously. Metal concentrations, sources and human health risk were studied. Results showed that both soil and road dust exhibited higher values for Mn and Zn and lower values for Co and Cd for the three fractions. Mass concentration ratios of PM(2.5)/PM(10) and PM(10)/PM(100) for metals in road and soil dust indicate that most of the heavy metals tend to concentrate in fine particles. Geoaccumulation index and enrichment factors analysis showed that Cu, Zn and Cd exhibited moderate or heavy contamination and significant enrichment, indicating the influence of anthropogenic sources. Vanadium, Cr, Mn and Co were mostly not enriched and were mainly influenced by crustal sources. For Ni, As and Pb, they ranged from not enriched to moderately enriched and were influenced by both crustal materials and anthropogenic sources. The conclusions were confirmed by multivariate analysis methods. Principle component analysis revealed that the major sources were vehicle emission, industrial activities, coal combustion, agricultural activities and crustal materials. The risk assessment results indicated that metal ingestion appeared to be the main exposure route followed by dermal contact. The most likely cause for cancer and other health risks are both the fine particles of soil and road dusts.     

Dynamics and origin of PM2.5 during a three-year sampling period in Beijing, China

Systematic sampling and analysis were performed to investigate the dynamics and the origin of suspended particulate matter smaller than 2.5 μm in diameter (PM(2.5)), in Beijing, China from 2005 to 2008. Identifying the source of PM(2.5) was the main goal of this project, which was funded by the German Research Foundation (DFG). The concentrations of 19 elements, black carbon (BC) and the total mass in 158 weekly PM(2.5) samples were measured. The statistical evaluation of the data from factor analysis (FA) identifies four main sources responsible for PM(2.5) in Beijing: (1) a combination of long-range transport geogenic soil particles, geogenic-like particles from construction sites and the anthropogenic emissions from steel factories; (2) road traffic, industry emissions and domestic heating; (3) local re-suspended soil particles; (4) re-suspended particles from refuse disposal/landfills and uncontrolled dumped waste. Special attention has been paid to seven high concentration "episodes", which were further analyzed by FA, enrichment factor analysis (EF), elemental signatures and backward-trajectory analysis. These results suggest that long-range transport soil particles contribute much to the high concentration of PM(2.5) during dust days. This is supported by mineral analysis which showed a clear imprint of component in PM(2.5). Furthermore, the ratios of Mg/Al have been proved to be a good signature to trace back different source areas. The Pb/Ti ratio allows the distinction between periods of predominant anthropogenic and geogenic sources during high concentration episodes. Backward-trajectory analysis clearly shows the origins of these episodes, which partly corroborate the FA and EF results. This study is only a small contribution to the understanding of the meteorological and source driven dynamics of PM(2.5) concentrations.     

北京市冬季大气细粒子数浓度的粒径分布特征

考虑到对人体的健康危害,大气颗粒物的数浓度值可能比质量浓度值更重要.通过对北京市交通道路边、生活区和远郊背景点大气细粒子数浓度的监测,对北京市大气细粒子数浓度的主要来源、浓度和粒径分布特征进行研究.文章认为交通源是城市大气细粒子数浓度的主要来源.城市生活区的大气细粒子主要是污染源稀释后扩散而来.远郊区既可能存在气象污染物光化学成核生成的超细颗粒物,也存在外部运移而来的细粒子.与国外其他城市相比,北京市大气细粒子数浓度在道路边处于中等偏下水平,但生活区和背景点处于相当或偏高的水平.     

杭州市燃煤废气中重金属排放清单建立

采用基于燃料消耗的排放因子法,以污染源普查动态更新数据为基础,建立了2010年杭州市燃煤废气中重金属(汞、砷、铅、镉、总铬、镍、锑等7种)排放清单。结果表明,2010年杭州市燃煤废气中汞、砷、铅、镉、总铬、镍、锑的年排放量分别为194.2、252.9、1 915.7、53.9、3 390.4、1 465.4、101.0 kg。燃煤废气中重金属的排放主要集中在燃煤消耗较高的拱墅区和江干区,其次是上城区,这3个区燃煤废气中重金属的排放量之和超过全市的95%。燃煤废气中重金属的排放量与燃煤量密切相关,但锅炉燃烧方式、除尘脱硫设施对重金属排放也起到了决定性作用。