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.     

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。     

西藏自治区“十二五”和“十三五”环境空气质量变化特征

"十二五"以来中国先后颁布了一系列大气污染防治政策并实施相应的大气污染治理措施以提高环境空气质量。为研究西藏自治区"十二五"(2011-2015年)和"十三五"(2016 -2020年)时期环境空气质量变化状况,评估大气污染防治措施实施效果,笔者对2011-2020年西藏自治区7个城市(地区)主要大气污染物浓度和优良天数比例变化进行分析。结果表明:随着一系列大气污染防治措施的实施,西藏自治区环境空气质量显著提升,与"十二五"时期相比,"十三五"时期西藏自治区年平均优良天数比例由97.5%±2.0%提升至99.3%±0.4%,SO₂、 NO₂、 PM₁₀、 PM_2.5浓度年均值和CO 浓度第95百分位数均呈下降趋势,其中CO、PM₁₀和PM_2.5浓度下降幅度较大,O₃日最大8h滑动平均值第90百分位数略有上升。西藏自治区环境空气质量与人口、经济发展程度呈负相关关系。受污染源排放、气象条件和区域传输等因素影响,西藏自治区O₃浓度春、夏季较高,而其余污染物浓度冬季较高。     

天水市大气SO2浓度时空变化特征的卫星遥感监测与影响分析

大气卫星遥感监测作为一种新型监测手段,具有范围广、速度快、成本低等优势,对环境应急保护及其预警都具有非常重要的意义。选取天水市2006—2013年各年12月每日OMI level-2数据产品,利用Aura卫星技术和Arc GIS等技术平台,对天水市大气中SO_2的时空分布规律和污染原因进行了分析研究。结果表明:2006—2013年天水市SO_2柱浓度及其总量呈现出明显增加的趋势,但在2008年有小幅降低;在2008年以前,天水市SO_2浓度呈现出由东南向西北逐渐减少的趋势,但自2009年后污染重心发生迁移并且出现了数个集中化的SO_2高值区;研究区SO_2垂直柱浓度有自然因素、人类活动等多方面的复合影响,其中能源消耗及机动车尾气排放是主要影响因素。研究进一步讨论了遥感数据产品的应用前景。     

临汾市大气污染物的时间分布特征与源区分析

多年来,临汾市多次名列我国生态环境部公布的空气质量最差的重点城市之列,对其大气污染的时间分布特征和潜在源区进行分析对其环境管理与污染防治具有重要意义。利用2015—2019年临汾市5个国控空气环境质量监测站点的6种空气污染物(SO₂、NO₂、CO、O₃、PM_2.5和PM₁₀)浓度数据和气象观测数据,使用HYSPLIT模型研究了该市空气污染物的时间变化特征、轨迹输送特征和可能的来源。结果表明,PM_2.5和PM₁₀的年均浓度均超过了《环境空气质量标准》(GB 3095—2012)Ⅱ级标准,SO₂仅在2016—2017年超过该标准,其余3种污染物的年均浓度均低于该标准。6种污染物2015—2019年的月均浓度的变化特征表现为O₃浓度呈以6、7月为中心的近似正态分布,SO₂、NO₂和CO以及PM_2.5和PM₁₀浓...     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

沈阳市冬季环境空气质量统计预报模型建立及应用

利用沈阳市2013年1—2月大气自动监测数据和同期气象资料,选取19项预报因子,采用逐步回归方法建立了沈阳市冬季环境空气质量统计预报模型,预报项目包括细颗粒物(PM2.5)、可吸入颗粒物(PM10)、二氧化硫(SO2)、二氧化氮(NO2)、一氧化碳(CO)日均浓度及臭氧(O3)日最大8 h平均浓度。2013年11月至2014年1月,应用该模型并结合人为经验修订,开展了沈阳市环境空气质量预报工作,预报结果与实测结果的对比验证结果表明,环境空气预测结果级别准确率达到79.1%,首要污染物准确率为73.6%。     

An Assessment on Variation of Sulphur Dioxide and Particulate Matter in Erzurum (Turkey)

Sulphur dioxide and PM₁₀ levels are investigated in Erzurum during the periods of 1990–2000 heating season to assess air pollution level. For that reason, emissions of sulphur dioxide and particulate matter were calculated by using consumption of fuels and Turkish emission factors. These emission values were evaluated together with air pollution levels, which were measured at six stations in Erzurum atmosphere during 1990–2000 winter periods. Results reveal that in 1990–1994 heating period, there is an increasing trend in the emissions and air pollution levels over Erzurum, and the air quality limits were not met. The daily 24 h limit (short-term limit) was exceeded 127 days in 1992–1993 winter period. The reason for this increase was found to be the switching to use of low-quality fossil fuels instead of cleaner ones. Results also indicated that there was a considerable decrease in emissions of air pollutants and air pollution levels after 1995. This can be explained by the consumption of more high-quality fossil fuels. The correlation coefficient of SO₂ with PM₁₀ is obtained as r² = 0.85, which is a high value supporting the idea that both pollutants are emitted from the same source.     

Evaluation of relationship between meteorological parameters and air pollutant concentrations during winter season in Elazığ, Turkey

In recent years, due to the rapid increase in population density, building density and energy consumption, the outdoor air quality has deteriorated in the crowded urban areas of Turkey. Elaz?? city, which is located in the east Anatolia region of Turkey, is also influenced by air pollutants. In the present study, relationship between monitored air pollutant concentrations such as SO₂ and the total suspended particles (TSP) data and meteorological factors such as wind speed, temperature, relative humidity, solar radiation and atmospheric pressure was investigated in months of October, November, December, January, February, and March during the period of 3 years (2003, 2004 and 2005) for Elaz?? city. According to the results of linear and non-linear regression analysis, it was found that there is a moderate and weak level of relation between the air pollutant concentrations and the meteorological factors in Elaz?? city. The correlation between the previous day’s SO₂, TSP concentrations and actual concentrations of these pollutants on that day was investigated and the coefficient of determination R² was found to be 0.64 and 0.54, respectively. The statistical models of SO₂ and TSP including all of meteorological parameters gave R² of 0.20 and 0.12, respectively. Further, in order to develop this model, previous day’s SO₂ and TSP concentrations were added to the equations. The new model for SO₂ and TSP was improved considerably with R²?=?0.74 and 0.61, respectively.     

基于KZ滤波法的江淮地区PM2.5浓度变化影响分析

基于2018—2020年合肥、芜湖和马鞍山3个城市国控站点的PM_2.5逐日监测数据和同期地面气象观测资料,利用Kolmogorov-Zurbenko(KZ)滤波对PM_2.5日浓度的原始时间序列进行分解,获取短期分量、季节分量和长期分量,并进行多元线性逐步回归构建各分量与气象因子的模型,最后依据短期分量和基线分量的回归模型和残差分析,对序列进行重建,获取消除气象条件影响的PM_2.5长期分量。KZ滤波分析结果表明：2018—2020年气象条件对江淮区域PM_2.5污染改善影响存在波动,在2018—2019年为负贡献,而在2020年秋冬季则变为正贡献;江淮地区3个城市2018年和2020年PM_2.5修正后的长期分量均值表明气象条件对各市PM_2.5改善影响存在差异较大,气象条件对合肥PM_2.5改善的贡献仅为1.0%,芜湖为7.8%,马鞍山为21.0%;NAQPMS数值模式情景分析结果显示,减排措施对江淮之间PM_2.5浓度改...     

Characteristics of ambient 1-min PM2.5 variation in Beijing

One-minute PM_2.5 concentration was obtained with LD-5C pocket microcomputer laser dust instrument from Dec. 15th, 2005 to Jan. 16th, 2006 and Mar. 17th to Apr. 28th, 2006 in Beijing. The concentration of SO₂, NO₂, O₃, CO, and PM₁₀ from Jan. 1st, 2001 to Dec. 31st, 2004 were obtained from the conversion of air pollution index. Results showed that all the pollutants showed cyclic characteristics. The longer yearly cycles was shown from SO₂, NO₂, O₃, CO, and PM₁₀, as the sampling time was 4-year long and daily collected. The shorter hourly and daily cycle was shown from 1-min PM_2.5, as the sampling time was about 1-month long and one collected at 1 min. The spectral density analysis confirmed this from the periodogram graphs. The longer yearly cycle (365, 180 days), the seasonal cycle (120, 60–90 days), and monthly cycle (21, 23, 27 days) of SO₂, NO₂, CO, O₃, and PM₁₀ were obviously shown. In addition, the shorter weekly cycle of 5–7 days is obviously shown, too. The shorter hourly cycle (8–12, 4–6, 3, 1–2 h, 20 min) of 1-min PM_2.5 was also indicated from spectral density analysis. Two major factors contribute the 1-min PM_2.5 cycles, i.e., the meteorological factors and source effects. Both the relative humidity and dew point showed consistent variation with PM_2.5, but the wind speed showed inverse variations with PM_2.5. Furthermore, the spectral density analysis of the meteorological factors (4–5, 2–2.5, 1–1.5 days, 12, 6–8, 3 h) may partially explain the cycles of PM_2.5. As for the sources effects, it can be shown from the strong dust storm of April 16–18th, 2006. PM_2.5 constantly increased tens and even hundreds of times high concentration within a few minutes due to the intensity of the dust sources.     

宜都市PM2.5与O3季节性分布特征及潜在源区分析

为了解宜都市PM_2.5与O₃的污染特征及潜在来源,利用宜都市2020年3月至2022年2月在线监测数据及气象数据,对宜都市PM_2.5与O₃质量浓度变化特征、气象影响因素及潜在源区进行了分析,结果表明:宜都市PM_2.5质量浓度冬高夏低,日变化呈双峰特征,O₃质量浓度夏高冬低,日变化呈单峰特征。高湿、静稳的气象条件以及较强偏北风作用下的区域污染传输对PM_2.5污染有重要影响,高温以及中湿度对O₃污染过程有重要作用。春、夏、秋季偏南方向气流轨迹占主导,且携带较高的污染物浓度,冬季来自湖北东北及西南方向的气流占比较高且携带的PM_2.5浓度较高;宜都市PM_2.5、O₃的潜在源区具有季节性差异,总体来看,主要分布在河南南部、湖北东部及湖南的北部区域。     

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.     

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。     

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).     

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。     

相似案例比对法在空气质量精细化预报中的应用

2018年首届中国国际进口博览会期间,为开展精细化的空气质量保障预报,以2015年11月13-16日为相似案例,与2018年11月8-11日上海地区的天气要素和PM2.5浓度变化进行相似性分析。结果显示,2个案例中地表压强、地表温度、相对湿度、混合层高度和风向5项主要天气要素的相关系数为0. 66~0. 93,相似离度为0. 09~0. 26,PM2.5浓度的相关系数达0. 8左右,相似离度为0. 2。针对2015年案例的污染过程分析,不仅为2018年案例中的污染时段预报提供了参考,也为空气质量保障工作的管控决策提供了支持。     

Photoacoustic measurements of black carbon light absorption coefficients in Irbid city, Jordan

The goal of this work is the analysis of air quality levels in the area of Volos, a city of average size on the eastern seaboard of Central Greece. For this purpose, concentration measurements of sulfur dioxide, nitrogen oxide, and nitrogen dioxide, for a 4-year period (2001–2004) are analyzed. Air pollution data were obtained by a monitoring station, fully automated, which was established by the Hellenic Ministry of the Environment, Physical Planning, and Public Works, in order to measure air pollution levels in Volos, a medium-sized city, which faces the effects of industrialization. The main conclusions from the statistical analysis of the 4-year measurements of hourly SO₂, NO₂, and NO concentrations in the city of Volos, showed that the mean seasonal variation of the examined air pollutant concentration presents a minimum during the warm period of the year and a maximum during the cold period. Although the local geomorphology and meteorology encourage particularly the accumulation of air pollutants, the analysis shows that the SO₂ and NO₂ concentration levels remain lower than corresponding thresholds for human health protection set by the European Union, in this urban measuring site, during the examined period. The application of harmonic analysis revealed the difference between the annual variation of the SO₂ and NO _x concentrations. Regarding NO _x , the variation is mainly due to the first harmonic term (anthropogenic factor), while the SO₂ variation is interpreted by the two harmonic terms, which represent the anthropogenic and meteorological factors, respectively.     

Assessment of Ambient Air Quality in Urban Centres of Haryana (India) in Relation to Different Anthropogenic Activities and Health Risks

Considering the mounting evidences of the effects of air pollution on health, the present study was undertaken to assess the ambient air quality status in the fast growing urban centres of Haryana state, India. The samples were collected for total suspended particulate matter (TSPM), respirable suspended particulate matter (PM₁₀), sulfur dioxide (SO₂), and oxides of nitrogen (NO₂) during different seasons from 8 districts of Haryana during January, 1999 to September, 2000. The four types of sampling sites with different anthropogenic activities i.e. residential, sensitive, commercial and industrial were identified in each city. The ambient air concentration of TSPM and PM₁₀ observed was well above the prescribed standards at almost all the sites. The average ambient air concentrations of SO₂ and NO₂ were found below the permissible limits at all the centres. Comparatively higher concentration of SO₂ was observed during winter seasons, which seems to be related with the enhanced combustion of fuel for space heating and relatively stable atmospheric conditions. Air Quality Index (AQI) prepared for these cities shows that residential, sensitive and commercial areas were moderately to severely polluted which is a cause of concern for the residents of these cities. The high levels of TSPM and SO₂ especially in winter are of major health concern because of their synergistic action. The data from Hisar city reveals a significant increase in the total number of hospital visits/admissions of the patients with acute respiratory diseases during winter season when the level of air pollutants was high.     

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。