Investigation of air pollution concentration in Kathmandu valley during winter season

The monthly concentrations of NO2, NOx, SO2 and O3 measured by a passive sampler from February 2003 to January 2004 showed that the air pollution during the winter season in Kathmandu valley was higher than the summer season. The O3 level was found the highest during April, May and June due to strong radiation. The hourly concentrations of NO2, NOx, O3 and suspended particulate matter（SPM） were also measured by automatic instruments on December 2003. Temperature at the height of 60 m and 400 m at Raniban Mountain in the northwest of Kathmandu valley was measured on February 2001 in the winter season and the average potential temperature gradient was estimated from observed temperature. Wind speed was also measured at the department of hydrology, airport section, from 18 February to 6 March 2001. It was found that the stable layer and the calm condition in the atmosphere strongly affected the appearance of the maximum concentrations of NO2 and SPM in the morning, and that the unstable layer and the windy condition in the atmosphere was considerably relevant to the decrease of air pollution concentrations at daytime. The emission amounts of NOx, HCs and total suspended particle（TSP） from transport sector in 2003 were estimated from the increasing rate of vehicles on the basis of the emission amounts in 1993 to be 3751 t/a, 30570 t/a and 1317 t/a, respectively. The diurnal concentrations in 2003 calculated by the two-layers box model reproduced the characteristics of air pollution in Kathmandu valley such as the maximum value of O3 and its time, the maximum value of NO in the morning, and the decrease of NO and NO2 at daytime. The comparison with the concentrations in 1993 calculated suggested that the main cause of air pollution was the emission from transport sector.     

Prediction of the environmental fate and aquatic ecological impact of nitrobenzene in the Songhua River using the modified AQUATOX model

An accidental discharge of nitrobenzene happened in November 2005 in the Songhua River,China.The AQUATOX model was modified and adapted to simulate the time-dependent nitrobenzene distribution in this multimedia aquatic system and its potential ecological impacts.Nitrobenzene concentrations in flowing water,sediment,and biota were predicted.Based on the initial concentrations of nitrobenzene observed in the field during the accidental discharge,that is,0.167-1.47 mg/L at different river segments, the pre...     

Secondary organic carbon quantification and source apportionment of PM10 in Kaifeng, China

During 2005, the filter samples of ambient PM10 from five sites and the source samples of particulate matter were collected in Kaifeng, Henan Province of China. Nineteen elements, water-soluble ions, total carbon (TC) and organic carbon (OC) contained in samples were analyzed. Seven contributive source types were identified and their contributions to ambient PM10 were estimated by chemical mass balance (CMB) receptor model. Weak associations between the concentrations of organic carbon and element carbon (EC) were observed during the sampling periods, indicating that there was secondary organic aerosol pollution in the urban atmosphere. An indirect method of “OC/EC minimum ratio” was applied to estimate the concentration of secondary organic carbon (SOC). The results showed that SOC contributed 26.2%, 32.4% and 18.0% of TC in spring, summer-fall and winter, respectively, and the annual average SOC concentration was 7.07 g/m3, accounting for 5.73% of the total mass in ambient PM10. The carbon species concentrations in ambient PM10 were recalculated by subtracting SOC concentrations from measured concentrations of TC and OC to increase the compatibility of source and receptor measurements for CMB model.     

Evaluation of the International Vehicle Emission （IVE） model with on-road remote sensing measurements

Intemational Vehicle Emissions （IVE） model funded by U.S. Environmental Protection Agency （USEPA） is designed to estimate emissions from motor vehicles in developing countries. In this study, the IVE model was evaluated by utilizing a dataset available from the remote sensing measurements on a large number of vehicles at five different sites in Hangzhou, China, in 2004 and 2005. Average fuel-based emission factors derived from the remote sensing measurements were compared with corresponding emission factors derived from IVE calculations for urban, hot stabilized condition. The results show a good agreement between the two methods for gasoline passenger cars＇ HC emission for all 1VE subsectors and technology classes. In the case of CO emissions, the modeled results were reasonably good, although systematically underestimate the emissions by almost 12%-50% for different technology classes. However, the model totally overestimated NOx emissions. The IVE NOx emission factors were 1.5-3.5 times of the remote sensing measured ones. The IVE model was also evaluated for light duty gasoline truck, heavy duty gasoline vehicles and motor cycles. A notable result was observed that the decrease in emissions from technology class State II to State I were overestimated by the IVE model compared to remote sensing measurements for all the three pollutants. Finally, in order to improve emission estimation, the adjusted base emission factors from local studies are strongly recommended to be used in the IVE model.     

Understanding unusually high levels of peroxyacetyl nitrate (PAN) in winter in Urban Jinan, China

Peroxyacetyl nitrate(PAN), as a major secondary pollutant, has gained increasing worldwide attentions, but relevant studies in China are still quite limited. During winter of 2015 to summer of 2016, the ambient levels of PAN were measured continuously by an automatic gas chromatograph equipped with an electron capture detector(GC–ECD) analyzer at an urban site in Jinan(China), with related parameters including concentrations of O3, NO, NO_2, PM_(2.5), HONO,the photolysis rate constant of NO_2 and meteorological factors observed concurrently. The mean and maximum values of PAN concentration were(1.89 ± 1.42) and 9.61 ppbv respectively in winter, and(2.54 ± 1.44) and 13.47 ppbv respectively in summer. Unusually high levels of PAN were observed during severe haze episodes in winter, and the formation mechanisms of them were emphatically discussed. Study showed that high levels of PAN in winter were mainly caused by local accumulation and strong photochemical reactions during haze episodes, while mass transport played only a minor role. Accelerated photochemical reactions(compared to winter days without haze) during haze episodes were deduced by the higher concentrations but shorter lifetimes of PAN, which was further supported by the sufficient solar radiation in the photolysis band along with the high concentrations of precursors(NO_2, VOCs) and HONO during haze episodes. In addition, significant PAN accumulation during calm weather of haze episodes was verified by meteorological data.     

Kinetics of hydrogen peroxide-sulfur (IV) reaction in atmospheric precipitation

The kinetics of the reaction of dissolved S(IV) with H2O2 was studied on 59 precipitation samples collected in Southwestern China, during the period of June 1988 to October 1989, from which the oxidation rates of the reaction were measured. The extent of reaction was followed by continuous amperometric measurement of the concentrations of the reagents. The two systems (i. e. rainwater and purified water) have been studied with the same methodology under identical reaction conditions (e. g. reagent concentrations, ionic strength and temperature). The kinetics was studied in no buffer solutions. The effect of formaldehyde on the reaction has also been studied, including kinetic studies in laboratory and model calculations, and it is indicated that formaldehyde with typical atmospheric concentration exerts no influence on the reaction of dissolved S(IV) with H2O2. In addition, the activation energy of the reaction was also measured in purified water as reaction medium for temperature range 0-50℃ at pH 4.0     

Investigating missing sources of glyoxal over China using a regional air quality model (RAMS-CMAQ)

Currently, modeling studies tend to significantly underestimate observed space-based glyoxal(CHOCHO) vertical column densities(VCDs), implying the existence of missing sources of glyoxal. Several recent studies suggest that the emissions of aromatic compounds and molar yields of glyoxal in the chemical mechanisms may both be underestimated, which can affect the simulated glyoxal concentrations. In this study, the influences of these two factors on glyoxal amounts over China were investigated using the RAMS-CMAQ modeling system for January and July 2014. Four sensitivity simulations were performed, and the results were compared to satellite observations. These results demonstrated significant impacts on glyoxal concentrations from these two factors.In case 1, where the emissions of aromatic compounds were increased three-fold,improvements to glyoxal VCDs were seen in high anthropogenic emissions regions. In case 2, where molar yields of glyoxal from isoprene were increased five-fold, the resulted concentrations in July were 3–5-fold higher, achieving closer agreement between the modeled and measured glyoxal VCDs. The combined changes from both cases 1 and 2 were applied in case 3, and the model succeeded in further reducing the underestimations of glyoxal VCDs. However, the results over most of the regions with pronounced anthropogenic emissions were still underestimated. So the molar yields of glyoxal from anthropogenic precursors were considered in case 4. With these additional mole yield changes(a two-fold increase), the improved concentrations agreed better with the measurements in regions of the lower reaches of the Yangtze River and Yellow River in January but not in July.     

Polar organic tracers in PM2.5 aerosols from an inland background area in Southwest China: Correlations between secondary organic aerosol tracers and source apportionment

PM_(2.5) aerosol samples were collected over 12 hr and 24 hr intervals in an inland background area, Gongga Mountain National Nature Reserve(hereafter shortened to Gongga), during the summer of 2011. Polar organic tracers, inorganic ions and meteorological data were measured. The purpose of this work was to investigate the variation patterns, formation and sources of the secondary organic aerosol tracers in the studied atmosphere. The average concentrations of isoprene oxidation products, α-pinene oxidation products, β-caryophyllinic acid, sugars, sugar alcohols and anhydrosugars were 88.6 ± 106.1, 3.6 ± 5.7,0.13 ± 0.30, 13.6 ± 13.1, 31.9 ± 31.4 and 14.8 ± 10.7 ng/m3 respectively in all aerosol samples.The aged α-pinene second organic aerosol(SOA) tracers(i.e., 3-hydroxyglutraric acid(3 HGA), 3-hydroxy-2,2-dimethylglutaric acid(HDMGA), 3-acetylpentandioic acid(APDA) and 3-methyl-1,2,3-butanetricarboxylic acid(MBTCA)) correlated significantly with each other in the 24 hr PM2.5 aerosol samples, indicating that OH· is the major factor controlling the formation of these α-pinene SOA tracers. Using the positive matrix factorization(PMF) model and the tracer-based source apportionment method, we calculated that isoprene oxidation products, α-pinene oxidation products, sesquiterpene oxidation products, biomass burning, fungi spores and anthropogenic SOA accounted for 21.9% ± 5.5%, 8.4% ± 2.1%, 3.0% ± 0.7%, 5.2% ± 5.3%, 5.0% ± 6.2% and 31.4% ± 7.8% of organic carbon respectively during the sampling period.     

Influencing factors and prediction of ambient Peroxyacetyl nitrate concentration in Beijing, China

Peroxyacyl nitrates (PANs) are important secondary pollutants in ground-level atmosphere. Accurate prediction of atmospheric pollutant concentrations is crucial to guide effective precautions for before and during specific pollution events. In this study, four models based on the back-propagation (BP) artificial neural network (ANN) and multiple linear regression (MLR) methods were used to predict the hourly average PAN concentrations at Peking University, Beijing, in 2014. The model inputs were atmospheric pollutant data and meteorological parameters. Model 3 using a BP-ANN based on the original variables achieved the best prediction results among the four models, with a correlation coefficient (R) of 0.7089, mean bias error of ? 0.0043 ppb, mean absolute error of 0.4836?ppb, root mean squared error of 0.5320?ppb, and Willmott's index of agreement of 0.8214. Based on a comparison of the performance indices of the MLR and BP-ANN models, we concluded that the BP-ANN model was able to capture the highly non-linear relationships between PAN concentration and the conventional atmospheric pollutant and meteorological parameters, providing more accurate results than the traditional MLR models did, with a markedly higher goodness of R. The selected meteorological and atmospheric pollutant parameters described a sufficient amount of PAN variation, and thus provided satisfactory prediction results. More specifically, the BP-ANN model performed very well for capturing the variation pattern when PAN concentrations were low. The findings of this study address some of the existing knowledge gaps in this research field and provide a theoretical basis for future regional air pollution control.     

Prediction of the concentration of chlorophyll-a for Liuhai urban lakes in Beijing City

The weekly water quality monitor data of Liuhai lakes between April 2003 and November 2004 in Beijing City were used as an example to build an artificial neural networks （ANN） model and a multi-varieties regression model respectively for predicting the fresh water algae bloom. The different predicted abilities of the two methods in Liuhai lakes were compared. A principle analysis method was first used to select the input variables of the models to avoid the phenomenon of collinearity in the data. The results showed that the input variables for the artificial neural networks were T, TP, transparency（SD）, DO, chlorophyll-a （Chl-a）,pH and the output variable was Chl-a. A three layer Levenberg-Marguardt feed forward leaming algorithm in ANN was used to model the eutrophication process of Liuhai lakes. 20 nodes in hidden layer and 1 node of output for the ANN model had been optimized by trial and error method. A sensitivity analysis of the input variables was performed to evaluate their relative significance in determining the predicted values. The correlation coefficient between predicted value and observed value in all data and in test data were 0.717 and 0.816 respectively in the artificial neural networks. The stepwise regression method was used to simulate the linear relation between Chl-a and temperature, of which the correlation coefficient was 0.213. By comparing the results of the two models, it was found that neural network models were able to simulate non-linear behavior in the water eutrophication process of Liuhai lakes reasonably and could successfully estimate some extreme values from calibration and test data sets.     

A numerical study of summer ozone concentration over the Kanto area of Japan using the MM5/CMAQ model

We assessed the ability of the MM5/CMAQ model to predict ozone (O3) air quality over the Kanto area and to investigate the factors that a ect simulation of O3. We find that the coupled MM5/CMAQ model is a useful tool for the analysis of urban environmental problems. The simulation results were compared with observational data and were found to accurately replicate most of the important observed characteristics. The initial and boundary conditions were found to have a significant e ect on simulated O3 concentrations. The results show that on hot and dry days with high O3 concentration, the CMAQ model provides a poor simulation of O3 maxima when using initial and boundary conditions derived from the CMAQ default data. The simulation of peak O3 concentrations is improved with the JCAP initial and boundary conditions. On mild days, the default CMAQ initial and boundary conditions provide a more realistic simulation. Meteorological conditions also have a strong impact on the simulated distribution and accumulation of O3 concentrations in this area. Low O3 concentrations are simulated during mild weather conditions, and high concentrations are predicted during hot and dry weather. By investigating the e ects of di erent meteorological conditions on each model process, we find that advection and di usion di er the most between the two meteorological regimes. Thus, di erences in the winds that govern the transport of O3 and its precursors are likely the most important meteorological drivers of ozone concentration over the central Kanto area.     

东亚边界层臭氧时空分布的数值模拟研究

利用嵌套网格空气质量预报模式系统(NAQPMS)对2010年东亚地区边界层臭氧(O3)的时空分布进行了数值模拟,并评估了东亚边界层光化学反应的活性.结果表明,NAQPMS模式与观测结果较为一致,站点观测与模拟的日均值(月均值)相关系数达到0.56~0.91,模式能合理再现东亚地区地面O3的时空分布特征.东亚地区冬季边界层O3低值区出现在中国东部;春季O3浓度增加,西北太平洋沿岸地区O3浓度达60μL/m3左右;夏季东亚中纬度35°N附近大陆地区O3由于强烈的光化学反应呈现出一浓度高值带,浓度达60μL/m3以上;秋季东亚大部分地区O3浓度维持在40~45μL/m3左右.夏季中国京津冀和长江三角洲部分地区光化学净生成率已超过30×10-9/d.     

城市空气质量模式及其预测研究

本文通过现场实测和研究,建立了一个适合于平原中等工业城市的空气质量模式体系,利用大量实测资料进行了模式验证,表明模式与实况符合甚好。在此基础上预测了1990年和2000年的大气污染状况,并进行了综合防治对策、环境容量估算等模式应用研究,取得良好效果。为城市经济发展和环境保护的协调调提供了科学依据。     

不同人为源排放对珠江三角洲地区O3生成贡献的数值模拟

利用改进的二维欧拉空气质量模式对珠江三角洲地区不同类型人为源排放对该区域臭氧生成的贡献进行了模拟研究.结果表明,珠江三角洲区域臭氧生成受VOCs排放控制,流动源排放VOC最多,对臭氧生成的贡献也最大,分别占臭氧最大小时浓度和平均小时浓度的44%和67%;其次是溶剂和油漆挥发的贡献.点源和面源排放VOC较少,但排放NOx量大,二者对臭氧生成影响是负的.控制所有类型人为源排放,珠江三角洲区域臭氧日均浓度和最大浓度将分别减少78%和90%.模拟结果还显示,受气象场和光化学反应共同影响,珠江三角洲南部区域是臭氧浓度高值区.     

2006～2012年珠三角地区空气污染变化特征及影响因素

利用粤港珠江三角洲区域空气监控网络2006~2012年监测结果,分析了珠三角地区SO2、NO2、O3和PM10浓度的年、月变化及空间分布特征,并对产生时空分布变化的原因进行了剖析.结果表明:7年来,珠三角地区SO2、NO2和PM10浓度呈下降趋势,降幅分别为61.7%、17.4%和24.3%,O3浓度呈上升趋势,增幅为12.5%,总体而言,珠三角地区空气质量呈好转趋势;湿季(4~9月)空气质量明显优于干季(10月至翌年3月),各污染物浓度的月变化均呈双峰型,SO2、NO2和PM10峰值浓度出现在12月和3月,O3峰值浓度出现在10月和5月;SO2、NO2和PM10浓度高值区主要集中在中部的广佛地区,O3浓度在外围郊区呈现高值,各部分地区的污染物浓度变化趋势不一致,中部经济核心区一次污染物浓度下降趋势更为显著.珠三角地区空气质量的变化受多方面因素的影响,经济下行和政府治理是驱动一次污染逐年好转的主要因素,而政府对VOCs排放控制相对薄弱,VOCs排放与气候变化的共同作用可能是导致二次污染(尤其是O3污染)加剧的原因.     

京津冀地区严重光化学污染时段O3的时空分布特征

利用MCCM(多尺度气象空气质量模式)对京津冀地区2008年6月严重光化学污染时段的近地面φ(NO_x)和φ(O₃)进行了模拟;同时,为了检验MCCM系统模拟φ(O₃)时空分布的能力,将模拟的气象要素、φ(NO_x)和φ(O₃)与观测数据进行了比对,并利用验证后的模拟结果对该地区严重光化学污染时段O₃时空分布特征进行研究. 结果表明:①MCCM模式可较好地反映气象场和污染物浓度场的时空分布特征. 气温、露点温度和气压的观测值与模拟值的相关系数分别为0.85、0.77和0.95;模拟的化学物种浓度的时空分布与观测结果基本相符. ②城市中心地区φ(NO_x)较高,北京和天津城市地区的φ(NO_x)甚至超过了30×10-9;京津冀平原大部分地区午后14:00φ(O₃)的最大值超过了70×10-9;而太行山沿线φ(O₃)的最大值超过了80×10-9. 结合气象要素的分析表明,午后φ(O₃)在太行山沿线的高值与气压场和流场关系密切. ③利用判断O₃生成敏感性指标——H₂O₂/HNO₃(体积分数比)分析发现,φ(O₃)日最大值和φ(总氧化剂)(总氧化剂=NO₂₊O₃)平均值的高值区域与O₃生成受NO_x和VOCs协同控制的区域极为吻合. 因此,要达到降低区域的光化学污染,应以VOCs的消减为主,同时兼顾NO_x的消减.      

珠江三角洲区域大气二次有机气溶胶的数值模拟

二次有机气溶胶是大气颗粒物污染的重要组分之一,确定大气中二次有机气溶胶污染状况及来源是深入了解大气颗粒物污染发生、演变规律及其影响因素的前提.基于珠江三角洲区域的污染源和气象资料,利用耦合了二次有机气溶胶模块的二维空气质量模式对区域尺度上的大气二次有机气溶胶污染状况和来源进行了模拟研究.结果表明,二次有机气溶胶生成具有明显的光化学反应特征,浓度高值出现在14:00左右;源排放较大的广州和东莞的部分地区及其下风向的中山、珠海和江门部分地区SOA浓度较高;几类主要污染源对SOA的贡献率分别为:生物源72.6%,流动源30.7%,点源12%,溶剂、油漆源12%,面源不足5%.     

济南市空气质量数值预报研究

在大规模大气环境参数综合野外测试资料的基础上,为在济南市开展大气污染业务预报新近开发了空气质量数值预报模式系统.该系统由污染源模型(SM)、下垫面参数化模型(XDM)、诊断模式(DM)、中尺度-α气象模式(M-αM)、中尺度-β气象模式(M-βM)、行星边界层(PBL)湍流统计量参数化模式(PBLTM)、干湿沉积模式(DWDM)和高分辨化学模式(HRCM)组成.该系统已成功地应用于济南市空气质量数值预报工作,其预报与实测质量浓度之间有很好的一致性,日平均预报准确率可达80%以上.      

珠三角秋季典型气象条件对空气污染过程的影响分析

利用空气质量指数(AQI)、主要大气污染物浓度和气象要素、天气图等数据资料,结合中尺度数值天气预报模式WRF,对2014年10月珠三角地区污染期间的天气形势及气象特征进行了分析.结果表明,WRF模式可以较好地反映珠三角地区主要城市地面和高空气象要素的时空变化,9个城市平均地表的温度、相对湿度和风速的模拟值与观测值的相关系数分别为0.90、0.87和0.78.对2014年10月3次污染过程的分析表明,造成该时段珠三角地区空气污染的天气形势主要是高压底部型和均压场型.静风或小风(2 m·s~(-1))及稳定的大气层结均不利于污染物的扩散,同时由于偏北气流输送周边污染物到珠三角地区,导致污染物浓度不断增加.相对湿度低于65%时,珠三角地区首要污染物以O_3为主;相对湿度高于70%时,PM_(2.5)浓度逐渐增加,成为主要污染物.高温等气象条件会影响光化学反应,加重珠江三角洲的空气污染,表现了该地区大气复合污染的特性.     

旅游和区域大气污染对四川九寨沟气溶胶的贡献

2010年4月~2011年4月,在九寨沟连续监测了总悬浮颗粒物(TSP)和水溶性无机离子的浓度.结果表明:旅游活动显著增加了空气中TSP、SO42-、Ca2+、K+、NH4+和NO3-的含量,而Na+、Mg2+和Cl-则主要来自自然源;旅游强度最高时期(6~10月),降水对空气的清洗作用最强,气溶胶污染程度为全年最低;旅游强度较低时期(1~3、4~5和11~12月),降水量较低且西北沙尘易在春季抵达九寨沟,所以气溶胶污染程度较高;区域燃煤可能是气溶胶[SO42-]:[NO3-]比值较高的原因;生物质燃烧可能是K+在秋末至次年初春较高的原因之一.九寨沟大气环境已明显受当地旅游活动和污染物长距离传输的影响.