2001年～2008年及奥运会期间天津市大气污染特征分析

根据天津市大气质量监测数据,对2001年～2008年及奥运会期间天津市大气污染特征和主要大气污染物的变化规律进行了分析。结果表明,2001年～2008年天津市的PM10、SO2和NO2污染总体呈下降趋势,但质量浓度仍相对较高。2008年8月奥运会期间天津市PM10和SO2质量浓度达到国家空气质量二级标准,NO2质量浓度达到国家空气质量一级标准,空气质量良好。天津市PM10污染相对稳定,SO2和NO2的污染分布呈现明显的季节性,时间上表现为冬强夏弱。气象条件对污染物浓度影响明显,沙尘、大雾等天气可使污染物浓度急剧升高。     

Investigating the impacts of some meteorological parameters on air pollution in Balikesir, Turkey

Air pollution is one of the most important environmental problems in Balikesir, situated in the western part of Turkey, during the winter periods. The unfavorable climate as well as the city’s topography, and inappropriate fuel usage cause serious air pollution problems. The air pollutant concentrations in the city have a close relationship with meteorological parameters. In the present study, the relationship between daily average total suspended particulate (TSP) and sulphur dioxide (SO₂) concentrations measured between 1999–2005 winter seasons were correlated with meteorological factors, such as wind speed, temperature, relative humidity and pressure. This statistical analysis was achieved using the stepwise multiple linear regression method. According to the results obtained through the analysis, higher TSP and SO₂ concentrations are strongly related to colder temperatures, lower wind speed, higher atmospheric pressure and higher relative humidity. The statistical models of SO₂ and TSP gave correlation coefficient values (R ²) of 0.735 and 0.656, respectively.     

Spatio-Temporal Variations of Sulphur Dioxide Patterns with Wind Conditions in Central Taiwan

Sulphur dioxide (SO₂) is one of the main atmospheric pollutants in central Taiwan. This article analyses the SO₂ concentration seasonal variations and spatial distribution using data obtained from ten air quality monitoring stations and the Taiwan Weather Bureau. It reveals that SO₂ concentration is high in winter and low in summer and that high concentration centers are located south of the Taichung coal-fired power plant, the main source of SO₂ emissions in the region.The location of high concentration centers changeswith different prevailing winds. SO₂ variations due towind direction are not unique. During short periods,when meteorological conditions are constant, variationin the pollution sources cause variations in thespatial distribution. This has been deduced byappreciation of Intervention analysis to time seriesof hourly data.     

Monitoring of Criteria Air Pollutants in Bursa, Turkey

The concentrations of criteria air pollutants such as CO, NO_x (NO + NO₂), SO₂ and PM were measured in the period of May 2001 and April 2003 in the city of Bursa, Turkey. The average concentrations for this period were 1115±1600 μg/m³, 29±50 μg/m³, 51±24 μg/m³, 79±65 μg/m³, 40±35 μg/m³, 98±220 μg/m³, for CO, NO, NO₂, NO_x, SO₂ and PM, respectively. Temporal changes in concentrations were analyzed using meteorological factors. Correlations among pollutant concentrations and meteorological parameters showed weak relations nearly in all data. Lower concentrations were observed in the summer months while higher concentrations were measured in the winter months. The increase in winter concentrations was probably due to residential heating. Pollutants were associated with each other in order to have information about their origin. NO_x/SO₂ ratio was also examined to bring out the source origin contributing on air pollution (i.e., traffic or stationary).     

Short-Term Forecasting of Nitrogen Dioxide (NO<Subscript>2</Subscript>) Levels Using a Hybrid Statistical and Air Mass History Modelling Approach

A novel hybrid model has been developed to support the provision of real-time air quality forecasts. Statistical techniques have been applied in parallel with air mass history modelling to provide an efficient and accurate forecasting system with the ability to identify high NO₂ events, which tend to be the episodes of most significance in Ireland. Air mass history modelling and k-means clustering are used to identify air mass types that lead to high NO₂ levels in Ireland. Trajectory matching techniques allow data associated with these air masses to be partitioned during model development. Non-parametric regression (NPR) has been applied to describe nonlinear variations in concentration levels with wind speed, direction and season and produce a set of linearized factors which, together with other meteorological variables, are employed as inputs to a multiple linear regression. The model uses an innovative integrated approach to combine the NPR with the air mass history modelling results. On validation, a correlation coefficient of 0.75 was obtained, and 91 % of daily maximum (hourly averaged) NO₂ predictions were within a factor of two of the measured value. High pollution events were well captured, as indicated by strong agreement between measured and modelled high percentile values. The model requires only simple input data, does not require an emission inventory and utilises very low computational resources. It represents an accurate and efficient means of producing real-time air quality forecasts and, when used in combination with forecaster experience, is a useful tool for identifying periods of poor air quality 24 h in advance. The hybrid approach outlined in this paper can easily be applied to produce high-quality forecasts of both NO₂ and additional pollutants at new locations/countries where historical monitoring data are available.     

Meteorological influence on predicting surface SO2 concentration from satellite remote sensing in Shanghai,China

In this study, we explored the potential applications of the Ozone Monitoring Instrument (OMI) satellite sensor in air pollution research. The OMI planetary boundary layer sulfur dioxide (SO₂_PBL) column density and daily average surface SO₂ concentration of Shanghai from 2004 to 2012 were analyzed. After several consecutive years of increase, the surface SO₂ concentration finally declined in 2007. It was higher in winter than in other seasons. The coefficient between daily average surface SO₂ concentration and SO₂_PBL was only 0.316. But SO₂_PBL was found to be a highly significant predictor of the surface SO₂ concentration using the simple regression model. Five meteorological factors were considered in this study, among them, temperature, dew point, relative humidity, and wind speed were negatively correlated with surface SO₂ concentration, while pressure was positively correlated. Furthermore, it was found that dew point was a more effective predictor than temperature. When these meteorological factors were used in multiple regression, the determination coefficient reached 0.379. The relationship of the surface SO₂ concentration and meteorological factors was seasonally dependent. In summer and autumn, the regression model performed better than in spring and winter. The surface SO₂ concentration predicting method proposed in this study can be easily adapted for other regions, especially most useful for those having no operational air pollution forecasting services or having sparse ground monitoring networks.     

烟花爆竹禁燃对春节期间东北地区空气质量影响评估

利用2015—2017年春节期间东北地区主要大气污染物(PM_(10)、PM_(2.5)、SO_2、NO_2、CO和O3)质量浓度监测资料及相应气象因子(温度、湿度、风速和气压)观测资料,分析了春节期间烟花爆竹禁燃对东北地区空气质量的影响。结果表明:随着东北地区主要城市禁燃力度的增强,空气质量逐年提升,PM_(2.5)和SO_2浓度逐年大幅度下降。禁燃可明显降低城区PM_(2.5)浓度,而由于春节期间污染源整体减少,城区和城郊监测点PM_(2.5)浓度值差异减小。烟花爆竹对PM_(10)和PM_(2.5)浓度影响高于对气体污染物SO_2、NO_2和CO的影响。此外,气象条件对东北地区春节期间禁燃改善空气质量的效果也有明显影响。因此,结合春节期间的气象条件,在东北地区实施禁燃政策动态调整非常必要。     

2018年11—12月北京市重污染天气过程数值预报能力评估

2018年11—12月北京市发生了4次以PM_2.5为首要污染物的重污染天气过程,为了分析数值模型对4次重污染过程的预报能力,将CMAQ模式提前1~7 d对北京市PM_2.5的小时预报结果与观测结果对比,分别从离散统计和分类统计2个方面评估CMAQ模式对4次重污染天气过程的预报效果,并简要分析了偏差产生的气象方面原因。结果表明：CMAQ模式提前1~6 d对重污染天气过程的预报显示出良好的性能,为日常业务预报提供了可借鉴的参考信息,可较好地预报出PM_2.5小时浓度变化趋势和浓度水平,离散统计结果显示提前1~4 d的预报结果好于提前5~7 d,相关系数r基本大于0.8,但有一定程度的低估趋势;分类统计结果显示不同预报时效预报准确率大于70%,探测准确率高于55%,部分时段可以达到80%~90%,对人工预报起到了良好的参考作用;输入的气象场的变化及其偏差对于重污染的起始时间、持续时间及清除时间有一定的影响,对相对湿度预报偏小和风速预报偏大是造成CMAQ模式低估的一个重要原因。     

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

This paper examines the application of artificial neural network (ANN) and boosted regression tree (BRT) methods in air quality modelling. The methods were applied to developing air quality models for predicting roadside particle mass concentration (PM₁₀, PM_2.5) and particle number counts (PNC) based on air pollution, traffic and meteorological data from Marylebone Road in London. Elastic net, Lasso and principal components analysis were used as feature selection methods for the ANN models to reduce the number of predictor variables and improve their generalisation. The performance of the ANN with feature selection (ANN hybrid) and the BRT models was evaluated and compared using statistical performance metrics. The performance parameters include root mean square error (RMSE), fraction of prediction within a factor of two of the observation (FAC2), mean bias (MB), mean gross error (MGE), the coefficient of correlation (R) and coefficient of efficiency (CoE) values. The input variables selected by the elastic net produced the best performing ANN models. The ANN hybrid produced models performed only slightly better than the BRT models. The R values of the ANN elastic net and BRT models were 0.96 and 0.95 for PM₁₀, 0.96 and 0.96 for PM_2.5 and 0.89 and 0.87 for PNC, respectively. Their corresponding CoE values were 0.72 and 0.70 for PM₁₀, 0.74 and 0.76 for PM_2.5 and 0.81 and 0.71 for PNC respectively. About 80–99% of all the model predictions are within a factor of two of the observed particle concentrations. The BRT models offer more advantages regarding model interpretation and permit feature selection. Therefore, the study recommends the use of BRT over ANN where the model interpretation is a priority.     

广州主要工业源排放的气态污染物扩散模拟研究

以广州省控工业污染源排放的气态污染物(SO₂、NO_x为主要研究对象,通过中尺度气象模式MM5与空气质量模式CALPUFF耦合,模拟11月典型气象条件下, SO₂和NO_x的扩散传输过程,研究其时空分布特征,并分析省控工业污染源排放对特定区域(主要针对2010年亚运场馆)空气质量的影响。结果表明,主要受典型风速的影响,SO₂和NO_x浓度具有明显的时空分布不均匀性。浓度高峰值主要出现在晚间至凌晨时段,而浓度低峰值主要出现在白天至中午时段。受污染源分布、排放高度和风向的影响,荔湾区和越秀区污染物浓度较高,且在广州西南部形成较明显的污染带;且这些省控污染源对南沙体育馆空气质量有较大影响。 研究结果对广州空气污染来源分析具有一定参考意义。     

Seasonal Differences in the Levels of Gaseous Air Pollutants in the Vicinity of a Waste Dump

Hydrogen sulphide, ammonia, nitrogen dioxide, mercaptans and sulphur dioxide (H₂S, NH₃, NO₂, R-SH, SO₂) concentrations were measured at the location in the vicinity of the waste dump to determine the air pollution level of these pollutants prior to the operation of the Mobile Thermal Treatment Plant. Samples were collected over one year period. Seasonal differences, and the influence of meteorological parameters (temperature, relative humidity, pressure and wind direction) on the air pollution levels were studied. Results show relatively low concentrations of H₂S, NO₂, R-SH and SO₂, while NH₃ levels were higher compared to the guideline values. Good weather conditions (high air pressure and low relative humidity) are connected to long range transport of NO₂, while higher temperatures result in elevated NH₃ and R-SH concentrations. Because of the predominant northeast wind direction (the same as the waste dump direction), the contribution of air pollution from the direction of the waste dump at the measuring site is significant, but that does not necessarily mean that the pollutants originated from that source.     

Effect Of Reagent (Dye) Addition In Wet Chemical Method Of Sulfur Dioxide Determination In Ambient Air

For measurement of sulfur dioxide (SO₂) in ambient air, the United States Environment Protection Agency (US EPA) had recommended the PRA dye-based colorimetric method as a reference technique. The method has been developed and applied in many countries for a longtime; however information regarding the sensitivity of the method with respect to sampling and analysis conditions is not available. Collaborative studies conducted in some of the South East Asian countries indicated substantial variations (of the order of 50%) in the measured concentrations against the true values. It was observed that dye used for color development plays an important role and even a slight variation in the way the dye solution is prepared can cause substantial variation in the measured SO₂ concentration. Because a major objective of air quality measurements is to relate air pollution concentration to the effects of air pollution, it is important that the method used should yield accurate levels of the pollutant, so that appropriate management plans can be devised and implemented effectively. In the present investigation, therefore, the role of dye used in SO₂ monitoring method has been highlighted. Different makes of dyes prepared in different modes were used to study the variation in the measured SO₂ concentration levels. Specifications of all the dyes were also tested. Need for use of certified reference material (CRM) for SO₂ has also been emphasized.     

重庆市北碚区空气质量改善效果评估分析

基于2014—2020年重庆市中心城区北碚区环境监测数据及地面观测气象要素,分析了北碚区大气污染特征,利用KNN算法建立大气污染的评估模型,对空气质量改善效果进行评估。结果表明,重庆市中心城区北碚区的PM_2.5浓度逐年呈明显下降趋势,O₃浓度除夏季有一个弱的下降趋势外,其余3个季节和年平均值整体均呈上升趋势。全年以优良天气为主且呈增加趋势。O₃与气温、日照时间呈正相关,与相对湿度呈负相关性,PM_2.5与气温、降水及风速呈负相关。基于KNN算法对空气质量改善状况评估表明,减排对O₃污染平均贡献率在-4.7%左右,对PM_2.5污染平均贡献率为-52%,气象条件对O₃污染的平均贡献率在17%左右,对PM_2.5污染的平均贡献率在-7%左右。该大气污染评估模型能够有效地评估空气改善效果。     

能源结构变化对兰州市大气质量的影响

将2001—2010年的主要能源材料消耗、主要污染物变化及主要行业分布进行了相关性分析,结果表明:兰州市大气4种主要污染物中除烟尘呈下降趋势外,废气总量、SO2、NOx仍呈上升趋势;主要污染源为燃煤,主要污染行业为电力、蒸汽热水产供业;废气和SO2排放总量与燃煤和燃气呈强正相关,而NOx排放只与燃气呈强正相关。建议发挥兰州地域优势,加大以水电供应为主的能源结构调整,对改善当地空气质量有重要价值。     

宁波市大气污染概况及影响因素分析

为研究宁波市大气污染状况及其影响因素，利用2013—2018年宁波市国控站点实时监测污染物数据以及气象数据，探讨分析了宁波市大气污染特征以及所受气象因素的影响概况。结果表明：宁波市颗粒物污染和O₃污染呈现典型的季节性特征，颗粒物浓度冬季最高，O₃最大滑动8 h平均质量浓度春、秋季最高。宁波市O₃污染问题越来越突出，且呈现出春、秋季O₃超标天数最多的季节变化特征。O₃小时质量浓度与气温和太阳辐射成正相关关系，NO₂和颗粒物浓度与气温成负相关关系。NO₂与O₃浓度成负相关关系，与颗粒物浓度成正相关关系。     

北京市典型重污染过程中PM2.5载带水溶性无机离子污染特征分析

为研究北京地区冬季PM_(2.5)载带的水溶性无机离子组分污染特征,2013年1月在中国环境科学研究院内采用在线离子色谱(URG-9000B,AIM-IC)对PM_(2.5)中水溶性无机离子(SO_4~(2-)、NO_3~-、Cl~-、NH_4~+、Na~+、K~+、Mg~(2+)、Ca~(2+))进行监测与分析。结果表明,采样期间总水溶性无机离子(TWSI)浓度为61.0μg/m~3,其中二次无机离子SO_4~(2-)、NO_3~-、NH_4~+(SNA)占比达72.3%,在PM_(2.5)中占比为40.29%,表明北京市PM_(2.5)二次污染严重。重污染天[NO_3~-]/[SO_4~(2-)]表明,固定源污染较移动源更为显著。三元相图表明,在空气质量为优的情况下,NH_4~+(在SNA中占比为30.3%~65.5%,下同)主要以NH_4NO_3的形式存在,较少比例以(NH_4)_2SO_4存在;严重污染时,NH_4~+(47.3%~77.9%)主要以(NH_4)_2SO_4形式存在,其次以NH_4NO_3的形式存在,其余的NH_4~+以NH_4Cl的形式存在。[NO_3~-]/[SO_4~(2-)]日变化表明,早、晚机动车高峰影响北京重污染发生。     

灰霾期间武汉城市区域大气污染物的理化特征

利用湖北省大气复合污染自动监测站2013年的全年监测数据,分析了灰霾期间武汉城市区域大气污染物的理化特征。霾日主要出现在春季、秋季和冬季。霾日与非霾日大气污染物质量浓度和气象参数的对比分析结果显示:高湿度、静风是武汉城市区域霾日的重要气象特征;PM1、PM_(2.5)、PM_(10)、NO_2、CO、NH3的质量浓度,SOR、NOR值以及PM_(2.5)中的二次无机离子(SO2-4、NO-3、NH+4)和部分元素(Pb、Se、Cd、Zn、K)的质量浓度均在霾日明显高于非霾日,而霾日SO2质量浓度仅在冬季略高于非霾日。选取2013年1月的连续灰霾日进行相关性分析,结果表明:污染组分主要来自当地排放(包括直接排放和二次形成),并受当地气象条件影响。此次灰霾过程中PM_(2.5)中的硫酸盐和硝酸盐主要来自气相反应,气态NO_2主要生成了气态HNO_3,而不是HNO_2。     

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。     

Status of air pollution caused by coal washery projects in India

The importance of coal washeries in India is growing as local coals have a high ash content. At present, there are 23 coal washeries with an annual rated input of 45 Million Tonnes. During the various operations in washeries, large amounts of dusts and gaseous pollutants are generated. Four coal washery projects were surveyed to study their air pollution characteristics. Air monitoring stations were set up in local industrial, residential and sensitive areas and air pollution samples were collected along with micro-meteorological data. Diurnal variations of SPM, SO₂ NO_x and CO are discussed. SPM concentrations were found to exceed the permissible limits at all locations. SO₂ and NO_x were also found to exceed the permissible limit in residential and sensitive areas. It was observed that about 50% of the dust particles were less than 10 µ in diameter. Benzene soluble matter in SPM ranged from 45–62%.     

Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques

Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO₂) into ozone (O₃) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O₃ in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O₃ concentration was tested. Results indicated that the presence of NO₂ and sunshine influence the concentration of O₃ in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.