华东森林及高山背景区域SO2、NOx、CO本底特征

国家大气背景监测福建武夷山站是中国华东区域背景站点之一，可代表华东森林及高山区域背景状况。为了解该区域的大气背景状况，评估区域污染现状以及污染物输送在区域污染中的作用，选取福建武夷山背景站2011年3月至2012年2月主要气体污染物（SO₂、NO_x、CO）为期1年的监测数据，研究各污染物在不同时间尺度的浓度变化特征和相关关系，以及与气象因子的相关关系，并利用后向轨迹模式探讨区域输送对华东森林及高山背景区域各气体污染物质量浓度的影响。结果表明，武夷山背景点监测期间SO₂、NO_x、CO的平均质量浓度分别为3.9、5.1、409.8 μg/m³，且具有明显的季节变化特征，春、冬季明显高于夏、秋季；三者日变化幅度均很小，呈现出单谷型分布型态，说明武夷山背景点受人为活动的影响很小，主要受气象条件影响；相关性分析结果显示，SO₂与NO_x浓度相关性较好，与湿度有较好的负相关，与风速在冬季具有一定的正相关，NO_x与CO浓度在秋季和冬季的相关性较好，且二者与温度的负相关性较好。后向轨迹分析结果表明，SO₂全年最大浓度峰值主要来自北方采暖季燃煤排放的远距离输送影响，NO_x、CO全年最大值则源于生物质燃烧的远距离输送影响。     

新冠肺炎疫情期间冀南地区空气质量时空变化特征

为研究新冠肺炎疫情期间冀南地区空气质量变化规律,明确防疫管控措施对空气质量的具体影响及大气污染物排放特征,笔者综合分析了研究区域的常规监测数据和部分大气超级站的PM_2.5组分数据。结果表明:疫情防控重点区域石家庄市、邢台市空气质量整体好转,细颗粒物和一次排放气态污染物浓度下降明显,PM_2.5来源中燃煤、生物质燃烧源占比上升,机动车尾气源占比下降,体现出交通管制、企业限产和道路工地扬尘管理等环保措施的有效性。疫情防控高风险区域石家庄市藁城区出现了明显的NO₂浓度降低、PM_2.5污染好转现象,而O₃浓度显著升高成为新的特征污染物。结果显示,藁城区综合防疫管控举措对本地一次排放污染物起到了明显抑制作用。疫情防控核心区域藁城区增村镇因实行最严格的封村、限行、停产等措施,人为污染源排放"触底",6项监测指标中除O₃浓度同比、环比均大幅度升高外,其他污染物浓度全时段降低,SO₂和CO昼夜差距缩小,环境质量明显优于周边乡镇。分析认为大规模持续化学消杀可能对O₃浓度升高有影响,此问题需要进一步探讨。     

基于Prophet模型的空气污染物浓度预测方法

准确预测大气污染物浓度的时空变化趋势是制定、修订大气污染防控措施和持续改善空气质量的关键。笔者以内蒙古呼和浩特市为研究区,基于2015-2020年城市环境空气质量自动监测站的月度数据,运用Prophet模型,研究确定环境监测数据的突变点和Prophet模型饱和预测上下限等特征,完成了"十四五"期间研究区PM_2.5、 PM₁₀、 NO₂、 SO₂、 O₃5项污染物的浓度预测。采用可决系数、均方根误差及平均偏差误差对预测模型结果进行精度评估,并重点分析了2025年各项污染物浓度的时空变化规律。结果表明:Prophet模型可以较为精准地对5项污染物浓度进行预测,预计2025年呼和浩特市PM_2.5、 PM₁₀、 NO₂、 SO₂、 O₃质量浓度分别为41.64,、71.71、36.85、21.66、123.24 μg/m³,与以往该地区各项污染物时空特征一致,未来该市PM_2.5、 PM₁₀、 NO₂、 SO₂全年浓度均呈U形分布,具有冬季高、夏季低的特点,O₃全年浓度呈倒V形分布,具有夏季高、冬季低的特点。除此之外,研究还发现未来该市西部地区污染程度高于东部。预测结果表明:当前内蒙古对空气污染的治理措施为空气污染的持续改善提供了保障,O₃污染有加重趋势但尚在可控范围内,需要进一步加强防控。该研究揭示了呼和浩特市"十四五"期间5项空气污染物的浓度和时空变化趋势,可为呼和浩特市空气质量持续改善提供数据参考。     

2001—2020年合肥市空气质量变化趋势研究

为系统分析合肥市长时间序列空气质量变化特征,对合肥市2001—2020年SO₂、NO₂和PM₁₀,以及2013—2020年CO、O₃和PM_2.5的浓度特征开展研究。采用Mann-Kendall(M-K)时间趋势检验法分析了6项污染物的时间变化规律,同时考虑了人为活动对污染物小时浓度的影响。结果表明,PM_2.5和O₃是目前影响合肥市空气质量的首要污染物。2014年以来,合肥市PM₁₀、PM_2.5、CO和SO₂年均浓度均呈逐年下降趋势,但NO₂和O₃污染有加剧趋势。合肥市SO₂和颗粒物浓度表现为冬春季节高、夏秋季节低;O₃浓度变化趋势与之相反;NO₂和CO浓度呈秋冬季节高、春夏季节低。     

2018年长三角一次持续重污染过程分析

利用常州市6个环境空气质量评价点PM_2.5、PM₁₀、SO₂、NO₂、CO和水溶性离子数据，结合后向轨迹、激光雷达探空资料、气象资料等，分析了2018年1月29日—2月2日长三角区域一次持续重污染过程。结果表明，重度污染时次高达94 h，PM_2.5最高值达235 μg/m³，由外来输送污染物与本地排放的污染物叠加而成，在不利气象条件影响下，污染物在长三角区域长时间滞留；重污染期间，污染物日变化规律显示，PM_2.5受外来源影响更显著，而SO₂、NO₂受本地污染源影响更显著，水溶性粒子组分与常州市本地源存在较大差异，其中NO₃^-、NH₄⁺、K⁺、Mg²⁺和SO₄^2-值增加最为明显，较污染前分别增加了9.1，5.9，4.3，4.2和4.1倍；K⁺值升高较快，说明污染期间也受到了生物质燃烧的影响。此外，NO₃^-和SO₄^2-在空气质量较好时，在水溶性离子中的占比日变化幅度较大，而在重污染期间，NO₃^-和SO₄^2-日变化幅度明显减小。     

2018年大连海域船舶大气污染物排放特征及影响分析

船舶废气已成为大气污染物的重要来源之一。为掌握大连周边海域船舶大气污染物排放特征,以及大连海域船舶排放对大连市大气环境的影响,基于船舶自动识别系统监测数据,采用基于功率的动力法估算出2018年大连海域船舶排放清单,并利用WRF、SMOKE、CMAQ空气质量数值模拟系统,研究了1月、4月、7月、10月船舶排放对大连市大气环境的影响。研究结果表明,2018年大连海域船舶共排放SO₂ 7 606.23 t、NO_x 30 990.13 t、PM₁₀ 1 212.02 t、PM_2.5 969.58 t、CO 3 339.10 t、HC 1 414.63 t、CO₂ 2 546 299.67 t。其中,客船、散货船、油船和多用途船的排放占比较大,排放区域主要集中在大连湾、大窑湾附近及周边航道。受气候因素影响,不同月份的船舶排放对大连市大气环境的影响不同,其中,1月、4月和10月受影响较大的区域主要是旅顺口区和金州区,而7月受影响较大的区域主要是甘井子区。     

2017年上半年北京市及周边地区空气质量特征分析

依托北京市、廊坊市和保定市高密度的地面空气质量监测、气象要素监测以及PM_2.5化学组分监测和后向轨迹分析等手段，对2017年上半年三地的空气质量进行分析。研究发现：三地中北京市空气质量较好，保定市较差。分污染物来看，保定市SO₂浓度水平明显高于廊坊市和北京市，颗粒物PM₁₀和PM_2.5也呈现保定市最高、北京市最低的规律。从污染物日变化来看，CO、SO₂、NO₂、PM₁₀和PM_2.5呈双峰型分布，O₃呈单峰型分布。从区域整体分布规律来看，PM_2.5和SO₂呈现明显的"南高北低"特征。PM_2.5化学组分分析结果表明：1—4月燃煤对该区域空气质量的影响较大，5—6月机动车排放的影响更为凸显。后向轨迹分析结果表明：在2017年上半年到达北京市的气流中有24%来自于北京市南部，且这些气流多为低空传输，表明区域传输对于北京市空气质量具有一定的影响。     

哈大绥城市“十三五”期间大气环境容量分析

根据2016—2020年哈尔滨市、大庆市、绥化市(以下称哈大绥)国控环境空气自动监测站的SO₂、NO₂、PM_2.5监测资料,统计年鉴中行政区划、污染物排放及气象等监测数据,分析哈大绥区域环境空气质量的变化趋势和测算因子,采用A值法核定了哈大绥区域SO₂、NO₂、PM_2.5的大气环境容量。结果表明:哈大绥SO₂、NO₂、PM_2.5 3项污染物采暖季的大气环境容量均呈逐年递增趋势。通过计算环境承载能力发现,哈大绥SO₂、NO₂、PM_2.5 3项污染物在非采暖季均具有高承载能力,哈大绥非采暖季环境容量高于采暖季。哈尔滨的个别污染物仍然处于临界超载状态,为减少重污染天气,应进一步削减采暖季污染物排放量。     

高精度船舶排放清单在城市空气质量管理中的应用

上海港船舶大气污染物排放对城市空气质量的影响不容小觑。基于船舶AIS数据，高精度船舶大气污染物排放清单得以建立并应用。2018年11月5—10日召开的第一届中国国际进口博览会期间，在气象条件不利的情况下，通过提前实施船舶排放控制区政策等措施，使船舶单日SO_x排放量下降28.5%，一次PM_2.5排放量下降25.5%，全市空气质量达到了保障要求。     

南通市区静态管理期间空气质量分析

对南通市区2022年4月初因疫情防控采取全区域静态管理期间的空气质量进行分析,以气象参数、臭氧前体物VOCs和NO_x作为分析对象。结果表明:此次污染过程的主导因素是高温、强辐射、低湿和偏南风的气象条件。南通市区处于VOCs控制区,高温、强辐射使得VOCs挥发性增强,浓度升高。偏南方向的苏通园区和能达公园VOCs浓度较高且升幅较大,源解析结果表明这2个点位涂料溶剂使用占比升幅更高,既容易受附近石化和储油库影响,也容易受偏南风向的污染输送影响。据初步统计,静态管理期间南通市区停工数量为80%左右,污染期间NO₂浓度高值区主要分布在沿江一带,长江南岸的张家港和常熟地区存在多家高排放企业,在偏南风下,张家港和常熟的污染物极易输送至南通市区。基于空气质量模型WRF-CAMx的O₃和PM_2.5来源解析结果显示,静态管理期间外来输送明显,占比为68.7%~84.7%。污染期间的船舶排放和二次转化贡献也不容忽视。建议南通市应重点加强工业、油气挥发和涂料溶剂源减排,同时加强区域联防联控,以便进一步改善空气质量。     

Model Assessment of Air-Pollution in Prague

Results of relatively simple gaussian dispersion model are presented. This model, developed in the beginning of this decade, is suitable mainly for determination of spatial distributions of annual mean concentrations of such kinds of air-pollution as sulphur dioxide (SO₂), mixture of nitrogen oxides (NO_x) or suspended particulate matter (SPM) from all types of emission sources located within the city - point sources, line sources and area sources. Model has been used in years 1994, 1996 and 1998 to assess the air-pollution distributions of the above mentioned kinds in the Prague area and the development of the air quality state. It contributed to the decision making process when possible impact of some changes in traffic system or in system of domestic heating, for example, has been investigated. In this contribution, behaviour of sulphur dioxide (SO₂) and mixture of nitrogen oxides (NO_x) ground concentration distributions are analyzed with respect to changing emission situation in Prague in recent years. Results show that SO₂ ground concentration level decreases mainly due to changes in local heating methods and type of fuel used. Different trend can be seen in NO_x ground concentrations thanks to rapid increase in the traffic density.     

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.     

Assessment of air pollution from small scale industry

Air pollution due to small scale industries have been found tocause serious occupational health hazards and adverse effects on vegetation and heritage. The study for air pollution impactassessment for brick kiln industries was undertaken. The stackmonitoring exercise was carried out to estimate the pollution level of SPM, SO₂, and NO_x. The ambient air quality was also measured in the vicinity of brick kilns to assess the impact of stack emission on ground level concentration. Characterisation of SPM for toxic metals were studied. Modelling exercise was carried to predict the impact of emission of brick kiln on surrounding environment. To minimise the emission level and ground level concentration, airpollution mitigation measures are suggested.     

Emissions of SO2, NO x and particulates from a pipe manufacturing plant and prediction of impact on air quality

Integrated pipe manufacturing industry is operation intensive and has significant air pollution potential especially when it is equipped with a captive power production facility. Emissions of SO₂, NO _x , and particulate matter (PM) were estimated from the stationary sources in a state-of-the-art pipe manufacturing plant in India. Major air polluting units like blast furnace, ductile iron spun pipe facility, and captive power production facility were selected for stack gas monitoring. Subsequently, ambient air quality modeling was undertaken to predict ground-level concentrations of the selected air pollutants using Industrial Source Complex (ISC 3) model. Emissions of SO₂, NO _x , and particulate matter from the stationary sources in selected facilities ranged from 0.02 to 16.5, 0.03 to 93.3, and 0.09 to 48.3 kg h^???1, respectively. Concentration of SO₂ and NO _x in stack gas of 1,180-kVA (1 KW = 1.25 kVA) diesel generator exceeded the upper safe limits prescribed by the State Pollution Control Board, while concentrations of the same from all other units were within the prescribed limits. Particulate emission was highest from the barrel grinding operation, where grinding of the manufactured pipes is undertaken for giving the final shape. Particulate emission was also high from dedusting operation where coal dust is handled. Air quality modeling indicated that maximum possible ground-level concentration of PM, SO₂, and NO _x were to the tune of 13, 3, and 18 μg/m³, respectively, which are within the prescribed limits for ambient air given by the Central Pollution Control Board.     

Assessment of Urban Air Quality in South Estonia by Simple Measures

In countries having limited resources, it is difficult to assess urban air quality on contemporaneously, due to the absence of on-line information about air pollution levels and emission rates. An alternative approach is recommended for smaller cities with lower demands of resources. The applied scheme consists of a database of air pollution sources (NO _x and CO from industry, traffic, and domestic heating), the simple Gaussian-plume model AEROPOL and a series of measurements by passive monitors. This method was used in Tartu, a small city situated in the valley of the river Emajõgi, within a landscape with noteworthy topographical variations. Simulations of annual average and maximal concentrations were performed, and a fair agreement obtained with NO₂ monitoring results from passive Palmes monitors. Inventories of pollution sources in 1998 revealed that official statistics of stationary sources covered 64% of SO₂,36% of CO, 37% of NO _x and 32% of total particulate matter emissions. Recommendations for measures for reducing air pollution levels and for further investigations towards improving air quality assessment and management, are given.     

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。     

上海市臭氧污染时空分布及影响因素

分析2006—2016年上海市的监测数据发现,臭氧(O_3)浓度存在逐年上升趋势,污染持续时间有所增加,但除水平风速有下降趋势外,其他相关气象因素的年际变化趋势并不显著。空间分析结果表明,上海市O_3超标主要集中在西南部郊区,但市区O_3超标潜势不容忽视。O_3污染高发季节的污染玫瑰图分析发现,上海市南部地区是影响上海市O_3污染的关键区域;对于NO_2减排的影响分析发现,尽管上海市O_3平均浓度总体处于上升趋势,但在NO_2下降幅度最为明显的内环市区和北部郊区,O_3上升幅度低于NO_2下降幅度较小的内外环区域和西部郊区,表明上海市的O_3污染控制仍需持续推进NOx的减排,并同步推进VOCs的减排。