渝东南典型城区冬季一次罕见的PM2.5污染过程及溯源分析

2020年12月底,以生态旅游业为主的重庆市渝东南地区出现了一次较为罕见的PM_2.5污染过程,持续时间长且污染程度重。以渝东南地区武隆区为例,应用污染特征雷达图、后向轨迹模型及潜在源污染贡献估算等方法分析了本次PM_2.5污染的特征及来源,结果表明：(1)在污染前期主要受扬尘、燃煤和机动车等污染排放影响,污染源直接排放贡献较大;中、后期污染受二次颗粒物影响显著,扬尘影响也较为明显。(2)污染期间的气流轨迹均为短距离输送,轨迹主要来自东北方向(65%)。(3)除自身污染排放贡献外,渝东北地区和主城都市区是武隆区PM_2.5污染的主要潜在源区,对武隆区传输贡献占比超50%。     

Estimating nutrient loadings using chemical mass balance approach

The river Hindon is one of the important tributaries of river Yamuna in western Uttar Pradesh (India) and carries pollution loads from various municipal and industrial units and surrounding agricultural areas. The main sources of pollution in the river include municipal wastes from Saharanpur, Muzaffarnagar and Ghaziabad urban areas and industrial effluents of sugar, pulp and paper, distilleries and other miscellaneous industries through tributaries as well as direct inputs. In this paper, chemical mass balance approach has been used to assess the contribution from non-point sources of pollution to the river. The river system has been divided into three stretches depending on the land use pattern. The contribution of point sources in the upper and lower stretches are 95 and 81% respectively of the total flow of the river while there is no point source input in the middle stretch. Mass balance calculations indicate that contribution of nitrate and phosphate from non-point sources amounts to 15.5 and 6.9% in the upper stretch and 13.1 and 16.6% in the lower stretch respectively. Observed differences in the load along the river may be attributed to uncharacterized sources of pollution due to agricultural activities, remobilization from or entrainment of contaminated bottom sediments, ground water contribution or a combination of these sources.     

基于数值模拟的青岛市O3快速来源解析

为了建立臭氧污染快速来源解析方法,提高解析结果的时空分辨率,以2017、2018年青岛市环境空气质量精细化管理实践为契机,利用CAMx-OSAT模型的污染源识别与追踪技术,预测解析未来时段特别是污染期间不同区域、行业的排放源对目标站点O3浓度的贡献量和贡献率。结果显示:模拟的春、夏季2个时段青岛市的O3来源主要为工业、电厂、交通、生活源,2个时段的模拟结果本地和外来源中工业源的贡献分别占62. 0%和65. 0%,交通源分别占24. 5%和16. 0%,生活源分别占8. 4%和8. 0%,电厂源分别占5. 0%和11. 0%,O3高浓度污染时,工业源的贡献上升明显,青岛市的O3浓度外来源的贡献约占40%~80%。该数值模型O3来源解析及预测方法已成功用于青岛市的空气质量精细化管理及应急管控。     

襄阳市秋冬季PM2.5污染特征及潜在源区分析

为了解襄阳市秋冬季PM_2.5的污染特征及来源，基于2020年11月至2021年1月在线监测数据，对PM_2.5质量浓度、气象因素、化学组分、来源及潜在源区进行了分析。结果表明，襄阳市秋冬季污染天首要污染物均为PM_2.5,且随污染程度加重，PM_2.5与PM₁₀质量浓度比呈上升趋势，二次颗粒物的形成对PM_2.5的贡献更高。在PM_2.5化学组分中，水溶性离子占比最大，随着污染程度加重，二次离子(SNA)快速增长，二次离子的生成转化是污染的重要成因。轻度、中度污染时，湿度高、风速小、气温低，有利于污染的积累，重度污染时湿度大、风速回升，有利于上游污染的输送与二次转化。PMF模型解析出襄阳市PM_2.5主要来源及贡献率为二次源58.0%、工业企业源22.6%、机动车源10.7%、扬尘源8.7%。襄阳市潜在源区主要分布在河南省中北部、河北省南部、山东省西部、安徽省北部、江汉平原东部及南部区域，极少量分布在襄阳区域，长距离区域传输...     

沈阳市环境空气中挥发性有机物污染特征及来源解析

基于2019年沈阳市4个不同功能区挥发性有机物(VOCs)小时分辨率的在线监测数据,分析了环境空气中VOCs的污染特征及来源。结果表明,观测期间沈阳市环境空气中VOCs日平均体积分数为(31.5±13.3)×10~(-9),4个功能区VOCs体积分数均呈现出冬季明显大于夏季的特征;工业区环境空气中VOCs体积分数明显高于其他功能区。商业交通居民混合区、文化居民混合区、郊区VOCs体积分数呈现明显双峰结构,工业区双峰结构不明显。工业区VOCs以新鲜排放为主,而其他3个区域为老化气团的传输。工业区春、夏季环境空气中VOCs来源包括燃料挥发源(26.90%)、溶剂与涂料源(17.69%)、燃烧源(16.40%)、化工源(15.69%)、交通源(7.57%)和炼油炼焦源(4.15%)。秋、冬季VOCs的来源包括燃烧源(30.77%)、溶剂与涂料源(20.26%)、燃料挥发源(18.79%)、化工源(11.54%)、炼油炼焦源(9.34%)和交通源(5.51%)。     

Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses

Water quality information of Beijiang River, a tributary of Pearl River in Guangdong, China, was analyzed to provide an overview of the hydrochemical functioning of a major agricultural/rural area and an industrial/urban area. Eighteen water quality parameters were surveyed at 13 sites from 2005 to 2006 on a monthly basis. A bivariate correlation analysis was carried out to evaluate the regional correlations of the water quality parameters, while the principal component analysis (PCA) technique was used to extract the most influential variables for regional variations of river water quality. Six principal components were extracted in PCA which explained more than 78% and 84% of the total variance for agricultural/rural and industrial/urban areas, respectively. Physicochemical factor, organic pollution, sewage pollution, geogenic factor, agricultural nonpoint source pollution, and accumulated pesticide usage were identified as potential pollution sources for agricultural/rural area, whereas industrial wastewaters pollution, mineral pollution, geogenic factor, urban sewage pollution, chemical industrial pollution, and water traffic pollution were the latent pollution sources for industrial/urban area. A multivariate linear regression of absolute principal component scores (MLR-APCS) technique was used to estimate contributions of all identified pollution sources to each water quality parameter. High coefficients of determination of the regression equations suggested that the MLR-APCS model was applicable for estimation of sources of most water quality parameters in the Beijiang River Basin.     

天津市开放源可吸入颗粒物污染问题分析

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。     

青藏高原典型城市拉萨市大气颗粒物污染源成分谱建立研究与特征分析

系统研究建立高原典型城市拉萨市开放源(土壤风沙尘、道路扬尘、施工扬尘、采矿扬尘),移动源(机动车尾气尘),固定源(工业烟粉尘、生物质燃烧尘及餐饮油烟)共3类8种大气颗粒物(PM_(2.5)、PM_(10))污染源化学成分谱。研究结果表明:开放源以地壳类元素为主,自然背景特征明显;移动源源成分谱中元素碳含量明显高于其他城市,在PM_(2.5)、PM_(10)源谱中分别占60.15%、51.86%,有机碳含量也相对较高,均超过20%;固定源中,牛粪和松柏枝两类生物质燃烧污染源的有机碳含量显著高于其他组分,工业烟粉尘中Ca远高于其他组分,在PM_(2.5)、PM_(10)源谱中分别占21.32%、21.21%。移动源、固定源源成分谱均显示出高原城市的独特特征。     

太湖流域典型厂村融合区复合面源污染特征分析——以礼嘉镇、洛阳镇、雪堰镇为例

为把握厂村融合区工农业复合面源污染现状及特征,选取典型区域礼嘉、洛阳、雪堰3镇进行研究,结果表明,2017年3镇复合面源等标污染负荷总量为1. 85×10~9m~3/a,其中总氮(TN)等标负荷量最高,占总量的44. 73%,为优先控制因子; 57个行政村中,污染物负荷量及负荷强度均较高的行政村大多集中在洛阳镇和礼嘉镇,如圻庄村、天井村、毛家村、大路村等,为优先控制区域;各污染源中,农村生活污水和畜禽养殖贡献的等标污染负荷量最高,分别占总量的37. 88%和35.49%,其次是种植业和厂区面源,贡献率分别为13. 17%和12. 77%,水产养殖贡献率最低;通过聚类分析将厂村融合区复合面源污染类型分为6类。     

基于主成分分析-多元线性回归的松花江水体中多环芳烃源解析

对松花江全流域14个监测断面的16种美国环保局优先控制的多环芳烃(PAHs)的主要来源及其贡献率应用主成分因子分析-多元线性回归模型(PCA-MLR)进行了来源解析。结果表明:松花江全流域为化石和石油燃料的复合PAHs污染,水体环境中PAHs首要污染源为化石燃料燃烧和交通污染,合计贡献率为63.1%,第二大污染源为工业和民用燃煤污染,合计贡献率为36.9%,沿江的石化、石油基地、大型焦化厂、电厂都是PAHs的主要来源。     

2018年11月上旬一次中韩PM2.5相互影响传输过程分析

2018年11月5—7日,韩国首尔出现了一次PM_(2.5)污染过程。利用拉格朗日轨迹分析(HYSPLIT)模型分析了首尔峰值浓度气团的来源,结合污染物监测和气象资料,定性分析了中国对韩国浓度高值可能的影响及其程度。利用嵌套网格空气质量预报模式(NAQPMS)及其耦合的在线污染来源追踪模块进行了污染来源解析和敏感性测试,分别计算了同一时期中韩两国相互间的PM_(2.5)传输贡献。结果显示:2018年11月5—7日,中国对韩国首尔污染过程的日均贡献不足10%;此次污染过程后期,首尔的污染气团对上海PM_(2.5)浓度峰值产生了影响。     

VOCs走航观测在城市污染源排查中的应用

为推进城市空气质量精细化管理工作的实施,实现VOCs污染源精准排查,2019年3-4月,利用单光子电离飞行时间质谱对青岛市重点区域进行了VOCs走航观测。在排查到的污染源中,工业区的VOCs浓度较生活区整体偏高,且生活区、工业区夜间的VOCs浓度均较白天高。VOCs各类组分中,生活区白天苯系物、卤代烃、烯烃、烷烃的占比均在20%左右,夜间苯系物占比明显升高;工业区苯系物在白天和夜间的占比均最高,其他组分相对较小。浓度较高的前10位VOCs物种中,生活区白天烯烃物种占主导,夜间烷烃物种的比重明显增加;工业区苯系物、烯烃物种在白天和夜间的比重均较大,烷烃物种较小。生活区VOCs的污染源主要为机动车尾气排放和油品挥发,工业区主要为企业排放。烯烃和苯系物臭氧生成贡献较烷烃高,特别是丁烯、戊烯、己烯、甲苯、二甲苯/乙苯、三甲苯贡献显著,建议作为优控物种重点管控。     

杭州秋冬季PM2.5污染特征分析

为研究杭州PM_2.5污染来源特征,利用2013—2019年杭州市PM_2.5监测数据和气象观测数据,分析了杭州市2013—2019年PM_2.5浓度变化,选取本地积累型和输入型2种PM_2.5污染过程,结合单颗粒气溶胶飞行时间质谱仪（SPAMS）和在线离子色谱数据,探讨杭州市PM_2.5化学组分和污染来源。结果表明：每年秋冬季（11月至次年3月）杭州以东北风、西北风及偏南风为主,风速低于4 m/s时,大气扩散条件差,受本地污染物积累影响,PM_2.5浓度容易出现超标;风速较大且为东北风和西北风时,受上游污染输入影响,易出现PM_2.5重度污染。本地积累型和输入型案例中,PM_2.5化学组分中占比最大的为NO₃^-、SO₄^2-和NH₄⁺;PM_2.5浓度上升过程中,二次NO₃^-和SO₄^2-转换率明显上升,其中NO₃^-上升更为显著,二次气溶胶污染严重。2次案例中,PM_2.5来源贡献占比前3位均为机动车尾气源、燃煤源和工业工艺源,其中本地积累型PM_2.5浓度上升阶段,机动车尾气源占比会明显上升;输入型案例中,输入阶段机动车尾气源占比显著上升,燃煤源贡献也小幅上升。     

濮阳市工业污染特征与产业布局调整路径探析

基于2017年濮阳市第二次全国污染源普查数据,采用聚类分析和核密度分析的方法对濮阳市行业结构特征、工业污染集聚特征进行了分析。结果表明,濮阳市支柱行业为石化行业,工业总产值占比36.92%,远高于其他行业;非金属矿物制品企业数量最多,污染物排放总量最高;颗粒物排放量占濮阳市工业污染源排放总量的33%,占比最高;非金属矿物制品业与石油、煤炭及其他燃料加工业是濮阳市废气污染物的主要来源,石油、煤炭及其他燃料加工业与农副产品加工业是濮阳市废水污染物的主要来源;濮阳市产业集聚区是污染物产排的重要区域,其污染物产生量占濮阳市污染物产生总量的95.49%,污染物排放量占濮阳市排放总量的63.05%。而非产业集聚区的污染物排放量占濮阳市排放总量的36.95%,是精准治污的重点。针对濮阳市产业结构和产业布局现状,提出,重新整合辖区非金属矿物制品业,继续做强石化行业,兼顾发展污染物排放强度低的行业;挖掘产业集聚区减排潜力,建设绿色产业集聚区是经济与环境协调发展的重要途径;非产业集聚区企业应因地制宜,政策引导,发挥中小企业自主能动性,提升环境质量。以期为濮阳市产业布局调整提供技术支持。     

蚌埠市夏季大气细颗粒物在线源解析

为了解蚌埠市环境空气中PM_(2.5)的来源,于2017年8月18日—9月18日,在百货大楼和高新区站点,利用单颗粒物气溶胶飞行时间质谱仪开展PM_(2.5)在线源解析。结果表明,百货大楼点位ρ(PM_(2.5))高于高新区点位,轻度污染比例(4.2%)明显高于高新区点位(0.8%),出现了中度污染(0.3%);SPAMS的PM_(2.5)质谱图显示百货大楼点位PM_(2.5)中K~+、Na~+特征明显,高新区点位HSO_4~-、NO_3~-、NO_2~-等无机信号较为明显;2个点位NO_3~-、NO_2~-、NH_4~+离子颗粒数占总颗粒数的百分比明显较高,且高新区点位NO_3~-、HSO_4~-离子数占比要明显高于百货大楼点位,燃料燃烧、工业工艺源、农田氮肥施用是其主要的人为污染源;2个点位PM_(2.5)成分主要为元素碳,分别占比42.4%,40.6%;污染时段,ρ(PM_(2.5))快速上升,除受本地机动车尾气源和燃煤源累积影响外,百货大楼点位扬尘源排放增加,高新区点位扬尘源和工业工艺排放源增加;2个点位机动车尾气源均为首要污染源,分别占比29.5%和30.9%,其次为燃煤源(24.3%和24.7%),扬尘源占比分别为22.9%和20.8%。     

工业污染源产排污系数存在问题分析及修订建议

通过对基层环境统计部门工业源产排污系数实际应用调研,结合当前环境保护重点工作和相关资料查阅等,分析了工业污染源产排污系数存在的主要问题:部分工业源产排污系数缺失,部分工业源产排污系数与实际偏差较大等。针对存在的主要问题提出了有优先顺序的修订建议,以期对第二次全国污染源普查工作提供有益参考。     

典型大气污染源动态排放清单编制方法及应用研究

随着环境空气质量精细化管理要求的提高,在应对重污染天气过程中,对大气污染源排放清单的时间分辨率提出了更高的要求。文章从固定源、移动源、油气储运源等方面探讨了大气污染源动态排放清单编制技术方法,并选取某城市进行了应用计算。结果表明,通过获取动态更新的数据,移动源的船舶、机动车、飞机可以逐时分析污染物排放量变化;固定源和油气储运源可以逐日分析。同时与相关指南算法进行定量比较,探讨了精细化改进方法及可能存在的问题,为建立更精细化动态排放清单提供有益参考。     

A Simple Model for Urban Background Pollution

A simple urban background pollution model is presented. Contributions from the individual area sources, subdivided into a grid net of a resolution of 2km × 2km, are integrated along the wind direction path assuming linear dispersion with the distance to the receptor point. Horizontal dispersion is accounted for by averaging the calculated concentrations over a certain, wind speed dependent wind direction sector, centred on the average wind direction. Formation of the nitrogen dioxide due to oxidation of nitrogen monoxide by ozone is calculated using a simple chemical model based on assumption of a photochemical equilibrium on the time scale of the pollution transport across the city area. The rate of entrainment of fresh rural ozone is governed by this time scale. The model is suitable for calculations of urban background when the dominating source is the road traffic. For this source the emissions take place at ground level, and a good approximation is to treat the emissions as area sources, but with an initial vertical dispersion determined by the height of the buildings.     

Application of integrated GIS and multimedia modeling on NPS pollution evaluation

In Taiwan, nonpoint source (NPS) pollution is one of the major causes of the impairment of surface waters. I-Liao Creek, located in southern Taiwan, flows approximately 90 km and drains toward the Kaoping River. Field investigation results indicate that NPS pollution from agricultural activities is one of the main water pollution sources in the I-Liao Creek Basin. Assessing the potential of NPS pollution to assist in the planning of best management practice (BMP) is significant for improving pollution prevention and control in the I-Liao Creek Basin. In this study, land use identification in the I-Liao Creek Basin was performed by properly integrating the skills of geographic information system (GIS) and global positioning system (GPS). In this analysis, 35 types of land use patterns in the watershed area of the basin are classified with the aid of Erdas Imagine process system and ArcView GIS system. Results indicate that betel palm farms, orchard farms, and tea gardens dominate the farmland areas in the basin, and are scattered around on both sides of the river corridor. An integrated watershed management model (IWMM) was applied for simulating the water quality and evaluating NPS pollutant loads to the I-Liao Creek. The model was calibrated and verified with collected water quality and soil data, and was used to investigate potential NPS pollution management plans. Simulated results indicate that NPS pollution has significant contributions to the nutrient loads to the I-Liao Creek during the wet season. Results also reveal that NPS pollution plays an important role in the deterioration of downstream water quality and caused significant increase in nutrient loads into the basin's water bodies. Simulated results show that source control, land use management, and grassy buffer strip are applicable and feasible BMPs for NPS nutrient loads reduction. GIS system is an important method for land use identification and waste load estimation in the basin. Linking the information of land utilization with the NPS pollution simulation model may further provide essential information of potential NPS pollution for all subregions in the river basin. Results and experience obtained from this study will be helpful in designing the watershed management and NPS pollution control strategies for other similar river basins.     

A holistic approach combining factor analysis, positive matrix factorization, and chemical mass balance applied to receptor modeling

Rapid urbanization and population growth resulted in severe deterioration of air quality in most of the major cities in India. Therefore, it is essential to ascertain the contribution of various sources of air pollution to enable us to determine effective control policies. The present work focuses on the holistic approach of combining factor analysis (FA), positive matrix factorization (PMF), and chemical mass balance (CMB) for receptor modeling in order to identify the sources and their contributions in air quality studies. Insight from the emission inventory was used to remove subjectivity in source identification. Each approach has its own limitations. Factor analysis can identify qualitatively a minimal set of important factors which can account for the variations in the measured data. This step uses information from emission inventory to qualitatively match source profiles with factor loadings. This signifies the identification of dominant sources through factors. PMF gives source profiles and source contributions from the entire receptor data matrix. The data from FA is applied for rank reduction in PMF. Whenever multiple solutions exist, emission inventory identifies source profiles uniquely, so that they have a physical relevance. CMB identifies the source contributions obtained from FA and PMF. The novel approach proposed here overcomes the limitations of the individual methods in a synergistic way. The adopted methodology is found valid for a synthetic data and also the data of field study.