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。