河谷地形气象要素对污染物浓度的影响

气象条件对环境空气质量影响的研究是科学制订大气环境污染防控方案、及时发布重污染天气应急预警的基础.利用兰州市西固区环境空气质量监测点2015年主要污染物小时浓度监测值,分析了污染物浓度的季节性变化和日变化特征,结合同期观测的气温、风速、相对湿度和降水量等气象资料,探讨了气象要素对污染物浓度变化的影响.结果表明:①除O₃外,其他污染物质量浓度呈"冬高夏低""早晚高下午低"的变化特点,这与表征污染物扩散能力的边界层参数变化特征一致. ②ρ（O₃）春季为71.42 μg/m3,夏季为62.74 μg/m3,春季高于夏季的主要原因是春季O₃的前体物NO_x质量浓度高于夏季且春季扩散条件弱于夏季. ③除O₃外,其他污染物质量浓度与气温、风速呈负相关. ④当相对湿度 < 40%时,污染物质量浓度与相对湿度呈正相关;当相对湿度>80%时,污染物质量浓度与相对湿度呈负相关;降雨对不同污染物的清除效果不同.研究显示,河谷地形气温和风速是影响污染物扩散的重要气象因子.      

天津污染天气边界层温度层结变化特征及预报阈值确定

针对天津市大气污染防治需求,基于2016年4月1日—2017年3月31日天津255 m气象塔观测资料及数值模拟,开展天津地区污染天气边界层温度层结变化特征及预报阈值研究.结果表明:(1)天津地区10~250 m高度的气温递减率为0.56℃/100 m,当日均气温递减率小于0.4℃/100 m时,垂直扩散条件不利于大气污染物扩散,出现中度以上污染概率为64%,重污染概率为47%.从温度廓线和逆温频率统计分析,贴地逆温占所有逆温的55%,除贴地逆温以外逆温底部最易出现在160 m的高度,大量脱地逆温的出现不利于高架源夜间的排放.(2)每年10月—次年2月天津逆温频率为20%,冬季需要关注逆温情况对大气污染物扩散的影响.如秋、冬季8:00逆温仍然存在,重污染天气出现概率高达56%,中度及以上污染出现概率为72%,是重污染天气辨识的重要指标.(3)7:00—10:00在逆温消散或者日均气温递减率由0.6℃/100 m向0.4℃/100 m变化时,任何细微变化对大气垂直扩散有显著影响.基于天津地区PM_(2.5)污染情况下,数值模拟显示10~250 m的气温递减率由于气溶胶的存在可减少0.06℃/100 m,在25个重污染过程中,日均气温递减率平均下降0.18℃/100 m,对大气垂直扩散条件产生显著影响.因此,在空气污染预报分析时使用不考虑气溶胶辐射效应的天气模式分析温度层结,需要适当调整阈值,尤其是在7:00—10:00逆温消散及垂直温度递减率由0.6℃/100 m向0.4℃/100 m变化时.     

北京市开发区PM1污染特征及影响霾形成的因素

通过采集北京市亦庄经济技术开发区2016年7月和10月、2017年1月和4月4个季节典型代表月大气亚微米颗粒物PM₁样品,分析研究了该开发区PM₁及其水溶性离子组分的季节变化以及不同污染时段的变化特征,揭示了影响二次组分形成和霾污染形成的重要因素.结果表明：研究期间开发区PM₁平均浓度为73.95μg/m³,高于北京市同期估算的PM₁平均水平,为其1.13倍.夏、秋、冬、春4季PM₁平均浓度分别为69.22,63.38,99.50,57.26μg/m³,明显呈现出冬季 > 夏季 > 秋季 > 春季的季节变化特征,各季节霾天PM₁浓度是清洁天的1.78~3.17倍.PM₁中总水溶性离子浓度为37.30μg/m³,占PM₁总质量浓度的50.44%,其中二次组分SO₄^2-、NO₃^-和NH₄⁺（SNA）平均浓度占总水溶性离子浓度的86.98%,是PM₁中水溶性离子的最主要组成部分.PM₁总水溶性离子浓度的季节变化与SNA的变化一致,表现为冬季 > 夏季 > 秋季 > 春季.研究期间硫氧化率（SOR）高于氮氧化率（NOR）,且SOR表现为夏 > 秋 > 冬 > 春,而NOR表现为夏 > 秋~春 > 冬,相应霾污染天SOR和NOR均显著高于清洁天,其中夏季霾天SO₂和NO₂的二次转化过程最为显著.SO₂向SO₄^2-的转化主要受相对湿度RH、温度T、NO₂以及NH₃的影响,且液相反应是硫酸盐形成的重要途径.NO₂向NO₃^-的转化受RH、T、O₃以及NH₃的影响较大.鞍型气压场、均压场、逆温层以及南、东南和西南方向为主的近地面偏弱气团传输是影响霾污染形成的重要因素.     

不同时间尺度气象要素与空气污染关系的KZ滤波研究

空气污染状况受气象要素和污染源排放的共同影响,为了评估大气污染控制措施的效果,需将由污染源排放的浓度数据分离出来.本文利用KZ滤波方法将天津市6个监测站点2015~2017年逐日的O₃、PM_2.5和PM₁₀浓度资料和6个同期气象数据分解为长期分量、短期分量和季节分量,计算各分量对原始时间序列方差的贡献.采用逐步回归法建立O₃及颗粒物3种分量与相应尺度气象要素的线性模型.结果表明,上述3种污染物浓度数据经分解后,季节分量对总方差贡献最大,其次为短期分量;气温和相对湿度是影响O₃季节和短期分量的主要气象因素,其中温度占主导地位,且呈现正相关,与相对湿度呈负相关;风速、气压、降水与颗粒物的短期及季节浓度变化呈负相关,相对湿度与之呈正相关,温度与短期分量呈正相关、与季节分量的变化呈负相关;经逐步回归消除气象影响的PM₁₀的长期分量有波动下降的趋势,PM_2.5浓度在2017年年初有所上升,其余部分有下降趋势,O₃长期分量浓度有所上升;这几年间颗粒物污染控制措施的效果较为显著,O₃污染有所加重.     

Seasonal evaluation and spatial variability of suspended particulate matter in the vicinity of a large coal-fired power station in India—A case study

Coal combustion in the power sector gives rise to the emission of primary and secondary particulate pollutants. Since the emission of pollutants depends on coal quality and combustion technology, and given that transport, transformation and deposition of contaminants depend on regional climatic conditions, specific studies for the power stations is needed to evaluate their environmental impacts. Monitoring of ambient respirable suspended particulate matter (RSPM) and suspended particulate matter (SPM) levels around a large coal-fired power station in India was carried out. The specific objectives were the determination of spatial and seasonal variability in RSPM and SPM levels, and their relationship with meteorological parameters such as wind velocity and relative humidity. The results have shown a marked seasonal trend and spatial variability in RSPM and SPM levels in the study area. Higher concentrations of ambient RSPM and SPM were found in downwind monitoring stations compared to upwind direction. Ratios of RSPM to SPM and correlation coefficient values between RSPM and SPM along with meteorological parameters were also worked out. Relative humidity and wind velocity have shown an inverse relation with particulate deposition pattern.     

沿海城市PM10和PM2.5自动监测过程中质量浓度“倒挂”现象分析与成因探讨

2020年2—3月,位于福建沿海地区中部的莆田市在环境空气质量自动监测过程中出现了严重的PM_(10)和PM_(2.5)质量浓度"倒挂"现象,小时值"倒挂"率为19.86%,日均值"倒挂"率为16.67%。在高相对湿度和低风速气象条件下,颗粒物会出现严重的"倒挂"现象,"倒挂"过程中常伴随着颗粒物和气态污染物(SO_2、NO_2和CO)质量浓度的增加。因此,于2020年2月16日—3月26日开展了颗粒物自动监测和手工监测比对,并结合气象参数、气态污染物质量浓度,以及PM_(10)和PM_(2.5)中水溶性离子和液态水的含量特征,进一步探讨了莆田市颗粒物质量浓度"倒挂"的主要成因。研究表明,PM_(10)和PM_(2.5)自动监测仪器检测原理的差异是导致颗粒物质量浓度"倒挂"的重要原因之一,而气象条件(相对湿度、气温和风速等)、颗粒物质量浓度、颗粒物中主要吸湿组分(NO_3~-、SO_4~(2-)和NH_4~+)和液态水的含量也是颗粒物质量浓度"倒挂"的主要影响因素。莆田市2020年2—3月出现高频率"倒挂"现象是多重因素共同作用的结果,解决该问题需要同时考虑监测仪器检测原理、气象参数、颗粒物质量浓度和吸湿组分等的影响。     

城市典型气象条件与大气颗粒物污染之间的关系

针对城市气象与城市气候特点,分析了典型气象条件下颗粒物的变化趋势,分析了由城市气候现象产生的城市效应问题,为研究空气中颗粒物来源、变化趋势奠定了基础.     

北京秋冬季空气严重污染的特征及成因分析

近年来北京大气环境质量达标天数明显增加,但是严重污染日依然占近6个百分点,并且没有明显减少的迹象。弄清严重污染,尤其是秋冬季节(9-12月,1-2月)积累型严重污染的时空分布特征和成因,对于污染预警、污染控制具有重要意义。文章首先分析了2000-2005年秋冬季节北京市大气污染达到4-5级的44个案例,发现该类严重污染具有发生时间连续、空间范围广和垂直散布高等特点。运用追踪过程的诊断分析方法剖析了2004年国庆期间和2005年11月初两个典型严重污染过程,说明了气象因子在秋冬季严重污染发生、持续和结束过程中的主导作用。文中最后给出了44个严重污染日气象条件的统计特征:850百帕温度偏高,可高于月均值5-15℃;海平面气压偏低;低空逆温发生概率大于75%;高湿度发生概率大,地面温露差在0-7.5之间约占80%;各层风速都很小,海平面0-2米/秒,850百帕风速2-16米/秒。     

First assessment of the PM<Subscript>10</Subscript> and PM<Subscript>2.5</Subscript> particulate level in the ambient air of belgrade city

INTENTION, GOAL, SCOPE, BACKGROUND: As the strong negative health effect of exposure to the inhalable particulate matter PM10 in the urban environment has been confirmed, the study of the mass concentrations, physico-chemical characteristics, sources, as well as spatial and temporal variation of atmospheric aerosol particles becomes very important. OBJECTIVE: This work is a pilot study to assess the concentration level of ambient suspended particulate matter, with an aerodynamic diameter of less than 10 microm, in the Belgrade central urban area. Average daily concentrations of PM10 and PM2.5 have been measured at three representative points in the city between June 2002 and December 2002. The influence of meteorological parameters on PM10 and PM2.5 concentrations was analyzed, and possible pollution sources were identified. METHODS: Suspended particles were collected on Pure Teflon filters by using a Mini-Vol low-volume air sampler (Airmetrics Co., Inc.; 5 l min(-1) flow rate). Particle mass was determined gravimetrically after 48 h of conditioning in a desiccator, in a Class 100 clean room at the temperature T = 20 degrees C and at about 50% constant relative humidity (RH). RESULTS AND DISCUSSION: Analysis of the PM10 data indicated a marked difference between season without heating--(summer; mean value 56 microg m(-3)) and heating season--(winter; mean value 96 microg m3); 62% of samples exceeded the level of 50 microg m(-3). The impact of meteorological factors on PM concentrations was not immediately apparent, but there was a significant negative correlation with the wind speed. CONCLUSIONS: The PM10 and PM2.5 mass concentrations in the Belgrade urban area had high average values (77 microg m(-3) and 61 microg m(-3)) in comparison with other European cities. The main sources of particulate matter were traffic emission, road dust resuspension, and individual heating emissions. When the air masses are coming from the SW direction, the contribution from the Obrenovac power plants is evident. During days of exceptionally severe pollution, in both summer and winter periods, high production of secondary aerosols occurred, as can be seen from an increase in PM2.5 in respect to PM10 mass concentration. RECOMMENDATION AND OUTLOOK: The results obtained gave us the first impression of the concentration level of particulate matter, with an aerodynamic diameter of less than 10 microm, in the Belgrade ambient air. Due to measured high PM mass concentrations, it is obvious that it would be very difficult to meet the EU standards (EEC 1999) by 2010. It is necessary to continue with PM10 and PM2.5 sampling; and after comprehensive analysis which includes the results of chemical and physical characterization of particles, we will be able to recommend effective control measures in order to improve air quality in Belgrade.     

论中国特色的高职环境教育--兼谈中国环境管理干部学院发展之路

本文在中国高等教育和环境保护的大背景下，分析了中国高职环境教育的迫切性与重要性，探讨了高职环境教育的中国特色及其内涵，并指出了中国环境管理干部学院如何走中国特色的高职环境教育发展之路。