杭州市区大气能见度变化趋势及其与主要污染物相关性分析

利用2004-2006年地面气象观测资料和同期环境空气质量自动监测数据,分析了杭州市区大气能见度变化趋势及其与主要污染物的相关性.结果表明,杭州市区能见度的日分布特征为14时最好,8时最差;季节变化特征为夏季>春季>秋季>冬季,全年仅7月能见度超过10 km;SO2、NO2、PM10浓度均随能见度增高而逐渐降低;影响能见度的首要因子为相对湿度和PM2.5,能见度与PM2.5浓度具有较好的相关性.     

浙东沿海城市大气颗粒物污染特征及来源解析研究

对2009年夏季浙东沿海地区环境空气质量进行监测,监测大气颗粒物(TSP、PM10、PM2.5、PM1.0)浓度,分析颗粒物污染特征、水溶性离子及无机元素组成,运用化学质量平衡受体模型(CMB模型)对浙东沿海地区大气TSP来源进行解析.结果表明,浙东沿海地区的大气颗粒物主要以细颗粒物为主,颗粒物中主要的水溶性离子为SO2-4、NH+4、Ca2+,土壤尘是该地区大气TSP的主要来源,北仑、乐清和奉化TSP中土壤尘的分担率分别达到55.49%、42.52%、40.70%,各监测点TSP来源具有一定的地域特征.     

成都市餐饮源大气污染物排放清单研究

为全面、准确地获得成都市餐饮源大气污染物排放清单,针对成都市社会餐饮、家庭餐饮和食堂餐饮分别选择监测对象进行细颗粒物(PM2.5)、非甲烷总烃(NMHCs)、油烟、氮氧化物(NOx)、SO2和CO 6种大气污染物排放浓度监测.分别按照用油量、就餐人次和灶头风量3种核算依据计算了6种大气污染物的排放因子,并计算成都市餐饮...     

上海市空气质量变化的多重分形分析

以上海市2000年7月至2006年6月的污染指数时间序列为基础数据,引入多重分形分析方法对上海市的大气污染特征及其变化趋势进行了研究.研究表明,上海市的3种主要大气污染物(SO2、NO2和PM10)在整个时间尺度上均表现出标度不变性,具有完全不同的多重分形特征.多重分形分析方法不仅能确认序列中的标度不变性,而且能说明3种大气污染物序列中概率分布的标度变化,这对于描述大气污染物时间序列的动力学变化具有现实意义.另外,进一步应用3个多重分形谱参数(B、△a和△f),研究了3种大气污染物各年的多重分形谱的变化,并结合上海市采取的大气环境治理措施,对其变化的原因进行了分析.结果表明,多重分形谱参数可作为一个评价城市空气质量演变程度的综合定量指标.为分析城市空气质量的演变提供了一条新的途径,对于认识上海市城市空气质量的变化过程和科学制订环境保护决策具有重要意义.     

重庆市核心区黑碳气溶胶浓度特征以及影响因素分析

2013年4月至2014年2月期间利用重庆市大气超级站的黑碳气溶胶(black carbon,BC)、气态污染物(SO2、NOx和O3)和颗粒物观测数据,分析了重庆市BC浓度的变化特征及与能见度、颗粒物以及SO2、NOx和O3气态污染物的相关性。观测期间BC年日均值为(4.86±2.37)μg/m3,浓度范围为1.32~11.54μg/m3。秋冬季BC日均浓度及相对偏差比春夏季高。BC和能见度呈负相关性。4个季度的BC与PM10、PM2.5和PM1日均值显著正相关,相关系数最小在夏季,最大在秋季。BC与O3日均值呈负相关性。BC与SO2,NOx日均值显著正相关,表明重庆市BC与SO2,NOx来源相近,即为燃煤和机动车尾气排放。     

城市大气中NOx和CO对质子交换膜燃料电池性能的影响研究

研究了城市主要大气污染物NOx和CO对质子交换膜燃料电池(PEMFC)性能的影响.电池恒流放电下的电压-时间曲线、极化曲线显示,体积分数为1×10-4左右的NOx对单电池性能造成严重的影响,但这种影响是可逆的.与此不同的是,往阴极通入7.9×10-5至1.56×10-3三种不同体积分数的CO65 h后,电池性能没有明显的下降.气相色谱和质谱数据表明,Pt能快速将空气中的CO氧化成CO2.     

小型燃油锅炉大气污染物的排放管理控制

燃料燃烧过程是大气污染物的重要来源之一,对人体健康,空气质量和气候变化具有非常重要的影响;管理控制是控制污染物排放的重要手段.以85台小型燃油锅炉(≤10.5 MW)燃料特性分析数据和污染物排放实测数据为基础,通过统计分析方法,分析了中国在用燃油品质以及大气污染物的排放现状,讨论了小型燃油锅炉大气污染物排放管理控制的潜力与可行性.结果表明,对燃油品质的管理控制是有效控制燃烧过程大气污染物排放的重要措施,分别有95%和98%燃油的灰分和含硫量符合国家相关规定;所有测试锅炉PM排放浓度远低于<锅炉大气污染物排放标准>(GB/T 13271-2001)规定的最高允许排放限值,有90%以上的锅炉达到GB/T 13271～2001中SO2最高允许排放限值,有84%的锅炉达到GB/T 13271-2001中NOx最高允许排放限值;与其他国家相比,中国对小型燃油锅炉常规大气污染物排放的管理控制处于中等水平,应当适时开展对有害空气污染物的管理控制.     

北京与伦敦空气中气态污染物的比对研究

城市空气质量问题已经引起广泛关注.通过对中英2个大城市北京与伦敦 2004 年 8 月～2005 年12 月空气中气态污染物 O3、NOx、SO2 和 CO 浓度变化的分析与对比发现:参照世界卫生组织空气质最准则、欧盟空气质量标准、美国国家空气质量标准或国家空气质量二级标准,北京O3、NO2、SO2和 CO 浓度的超标天数或时数明显高于伦敦.观测期内,北京 O3、NOx、SO2 和 CO 浓度明显高于伦敦,平均值分别是 17.9±22.1×10-9、72.4±76.1×10-9、19.5±21.8×10-9、2 004.6±1 509.8×10-9与10.8±9.9×10-9、54,6±38.9×10-9、1.8±2.2×10-9、372.3±235.0×10-9.两城市 O3 统计日变化形式均表现为白天高、夜晚低,峰值出现在午后 14:00 左右,日较差分别为 31.5±30.9×10-9与 11.1±7.7×10-9;NO、NO2、SO2 和 CO 呈双峰态日变化,峰值出现在交通的早高峰与晚高峰附近.北京 O3 最高值出现在夏季,而伦敦出现在春季;但两城市NOx、SO2 和 CO 最高值均出现在冬季.北京与伦敦的NO2与 NO 呈显著线性相关,且斜率与截距十分相似,分别是 1.25 和 1.28 与 28.1 和 23.2;同时两城市 CO/NOx 比率明显高于 SO2/NO 分别为 14.0、4.5 与 0.13、0.03.由此可以判断:对于两城市空气污染问题,交通源的贡献要远大于点源;但点源也对两城市空气质量造成影响.此外,连续逆温的天气是造成重污染事件的原因.     

江苏省污染减排与空气质量变化相关性分析

利用2000—2007年大气污染物排放量数据和同期环境空气质量监测数据,分析了江苏省主要大气污染物减排与环境空气质量变化的相关性。结果表明,近年来江苏省SO2排放量与环境空气中SO2浓度存在正相关,而烟尘和粉尘排放总量与空气中可吸入颗粒物的浓度呈现出弱的负相关关系。对江苏省经济发展和环境关系的进一步分析揭示,江苏省环境库兹涅茨曲线呈现出倒U型关系,表明江苏省已经进入经济环境双赢区间,但近年来政策对经济环境关系的影响突出。该研究对中国十二五环境管理政策的制定有着重要的参考意义。     

尿素和添加剂湿法烟气同时脱硫脱氮工艺研究(Ⅰ)

对尿素和添加剂同时吸收烟气中SO2和NOx进行了实验研究.结果表明,烟气中SO2极易脱除,在实验条件下SO2脱除率均大于99%,操作工艺条件变化主要是影响NOx脱除率.尿素和添加剂质量分数对NOx脱除率影响较小,NOx脱除率随尿素和添加剂质量分数的增加而缓慢增加;吸收剂pH和吸收反应温度对NOx脱除率有显著影响,最佳pH为7,最佳反应温度为70～80℃.     

A critical assessment of shrinkage-based regression approaches for estimating the adverse health effects of multiple air pollutants

Most investigations of the adverse health effects of multiple air pollutants analyse the time series involved by simultaneously entering the multiple pollutants into a Poisson log-linear model. Concerns have been raised about this type of analysis, and it has been stated that new methodology or models should be developed for investigating the adverse health effects of multiple air pollutants. In this paper, we introduce the use of the lasso for this purpose and compare its statistical properties to those of ridge regression and the Poisson log-linear model. Ridge regression has been used in time series analyses on the adverse health effects of multiple air pollutants but its properties for this purpose have not been investigated. A series of simulation studies was used to compare the performance of the lasso, ridge regression, and the Poisson log-linear model. In these simulations, realistic mortality time series were generated with known air pollution mortality effects permitting the performance of the three models to be compared. Both the lasso and ridge regression produced more accurate estimates of the adverse health effects of the multiple air pollutants than those produced using the Poisson log-linear model. This increase in accuracy came at the expense of increased bias. Ridge regression produced more accurate estimates than the lasso, but the lasso produced more interpretable models. The lasso and ridge regression offer a flexible way of obtaining more accurate estimation of pollutant effects than that provided by the standard Poisson log-linear model.     

An investigation of distributed lag models in the context of air pollution and mortality time series analysis

In particulate air pollution mortality time series studies, the particulate air pollution exposure measure used is typically the current day's or the previous day's air pollution concentration or a multi-day moving average air pollution concentration. Distributed lag models (DLMs) that allow for differential air pollution effects that are spread over multiple days are seen as an improvement over using a single- or multi-day moving average air pollution exposure measure. However, at the current time, the statistical properties of DLMs as a measure of air pollution exposure have not been investigated. In this paper, a simulation study is used to investigate the performance of DLMs as a measure of air pollution exposure in comparison with single- and multi-day moving average air pollution exposure measures under various forms for the true effect of air pollution on mortality. The simulation study shows that DLMs offer a more robust measure of the effect of air pollution on mortality and avoid the potential for a large negative bias compared with single- or multi-day moving average air pollution exposure measures. This is important information. In many U.S. cities, particulate air pollution concentrations are observed only once every six days, meaning it is often only possible to use single-day particulate air pollution exposure measures. The results from this paper will help quantify the magnitude of the negative bias that can result from using single-day exposure measures. The implications of this work for future air pollution mortality time series studies are discussed. The data used in this paper are concurrent daily time series of mortality, weather, and particulate air pollution from Cook County, IL, for the period 1987-1994.     

Workplace and Environmental Air Contaminant Concentrations Measured by Open Path Fourier Transform Infrared Spectroscopy: A Statistical Process Control Technique to Detect Changes from Normal Operating Conditions

Open path Fourier transform infrared (OP-FTIR) spectroscopy is a new air monitoring technique that can be used to measure concentrations of air contaminants in real or near-real time. OP-FTIR spectroscopy has been used to monitor workplace gas and vapor exposures, emissions from hazardous waste sites, and to track emissions along fence lines. This paper discusses a statistical process control technique that can be used with air monitoring data collected with an OP-FTIR spectrometer to detect departures from normal operating conditions in the workplace or along a fence line. Time series data, produced by plotting consecutive air sample concentrations in time, were analyzed. Autocorrelation in the time series data was removed by fitting dynamic models. Control charts were used with the residuals of the model fit data to determine if departures from defined normal operating conditions could be rapidly detected. Shewhart and exponentially weighted moving average (EWMA) control charts were evaluated for use with data collected under different room air flow and mixing conditions.

Under rapidly changing conditions the Shewhart control chart was able to detect a leak in a simulated process area. The EWMA control chart was found to be more sensitive to drifts and slowly changing concentrations in air monitoring data. The time series and statistical process control techniques were also applied to data obtained during a field study at a chemical plant. A production area of an acrylonitrile, 1,3-butadiene, and styrene (ABS) polymer process was monitored in near-real time. Decision logics based on the time series and statistical process control technique introduced suggest several applications in workplace and environmental monitoring. These applications might include signaling of an alarm or warning, increasing levels of worker respiratory protection, or evacuation of a community, when gas and vapor concentrations are determined to be out-of-control.     

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.     

Forecasting air quality time series using deep learning

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O₃) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O₃ exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours.

Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.     

Seasonal variation of air pollution index: Hong Kong case study

Air pollution is an important and popular topic in Hong Kong as concerns have been raised about the health impacts caused by vehicle exhausts in recent years. In Hong Kong, sulphur dioxide SO2, nitrogen dioxide (NO2), nitric oxide (NO), carbon monoxide (CO), and respirable suspended particulates (RSP) are major air pollutants caused by the dominant usage of diesel fuel by goods vehicles and buses. These major pollutants and the related secondary pollutant, e.g., ozone (O3), become and impose harmful impact on human health in Hong Kong area after the northern shifting of major industries to Mainland China. The air pollution index (API), a referential parameter describing air pollution levels, provides information to enhance the public awareness of air pollutions in time series since 1995. In this study, the varying trends of API and the levels of related air pollutants are analyzed based on the database monitored at a selected roadside air quality monitoring station, i.e., Causeway Bay, during 1999-2003. Firstly, the original measured pollutant data and the resultant APIs are analyzed statistically in different time series including daily, monthly, seasonal patterns. It is found that the daily mean APIs in seasonal period can be regarded as stationary time series. Secondly, the auto-regressive moving average (ARMA) method, implemented by Box-Jenkins model, is used to forecast the API time series in different seasonal specifications. The performance evaluations of the adopted models are also carried out and discussed according to Bayesian information criteria (BIC) and root mean square error (RMSE). The results indicate that the ARMA model can provide reliable, satisfactory predictions for the problem interested and is expecting to be an alternative tool for practical assessment and justification.     

Changes in concentration levels of selected VOCs in newly erected and remodelled building in Gdansk

Volatile organic compounds such as benzene, toluene, butyl acetate, ethylbenzene, m-xylene, styrene and m-dichlorobenzene were measured in three newly erected and remodelled dwellings. The present study also attempted to examine the time dependence of concentrations of selected VOCs in each investigated dwelling. This was accomplished by at least triplicate measurements of the IAQ. To collect a series of air samples the active and passive methods were used. In both cases activated charcoal was applied as a sorption medium. The samples were recovered by solvent extraction, and analysed by capillary column gas chromatography, employing a flame ionisation detector. The experimental results showed that MAC values for analysed VOCs were exceeded (even a few orders of magnitude) for the measurements made before inhabiting of the occupants, in every investigated dwelling. The concentrations of the investigated VOCs decreased significantly with time, which should be expected, although in some cases the levels of selected VOCs remained still high. Our experience indicates that parallel application of two different indoor air sampling techniques to determine analytes of interest, though more laborious and time consuming, can lead to significant conclusions concerning indoor air quality in monitored spaces.     

Evaluating the performance of regional-scale photochemical modeling systems: Part II—ozone predictions

In this paper, the concept of scale analysis is applied to evaluate ozone predictions from two regional-scale air quality models. To this end, seasonal time series of observations and predictions from the RAMS3b/UAM-V and MM5/MAQSIP (SMRAQ) modeling systems for ozone were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the scale analysis approach can help improve our understanding of the models’ performance. The results indicate that UAM-V underestimates the total variance (energy) of the ozone time series when compared with observations, but shows a higher mean value than the observations. On the other hand, MAQSIP is able to better reproduce the average energy and mean concentration of the observations. However, both modeling systems do not capture the amount of variability present on the intra-day time scale primarily due to the grid resolution used in the models. For both modeling systems, the correlations between the predictions and observations are insignificant for the intra-day component, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and baseline components. This better model performance on longer time scales suggests that current regional-scale models are most skillful in characterizing average patterns over extended periods, rather than in predicting concentrations at specific locations, during 1–2 day episodic events. In addition, we discuss the implications of these results to using the model-predicted daily maximum ozone concentrations in the regulatory framework in light of the uncertainties introduced by the models’ poor performance on the intra-day and diurnal time scales.     

Evaluating the performance of regional-scale photochemical modeling systems: Part I—meteorological predictions

In this study, the concept of scale analysis is applied to evaluate two state-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal time series of observations and predictions for temperature, water vapor, and wind speed were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the method of spectral decomposition can help provide additional insight into the models’ performance. The results indicate that both meteorological models under-represent the variance of fluctuations on the intra-day time scale. Correlations between model predictions and observations for temperature and wind speed are insignificant on the intra-day time scale, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and longer-term components. This better model performance on longer time scales suggests that current regional-scale models are most skillful for characterizing average patterns over extended periods. The implications of these results to using meteorological models to drive photochemical models are discussed.     

Relating meteorological variables and trends in motor vehicle emissions to monthly urban carbon monoxide concentrations

A model which quantifies the relationship between the monthly time series for CO emissions, the monthly time series in ambient CO concentration, and meteorologically driven dispersion was developed. Fifteen cities representing a wide range of geographical and climatic conditions were selected. An eight-year time series (1984–1991 inclusive) of monthly averaged data were examined in each city. A new method of handling missing ambient concentration values which is designed to calculate city-wide average concentrations that follow the trend seen at individual monitor sites is presented. This method is general and can be used in other applications involving missing data. The model uses emissions estimates along with two meteorological variables (wind speed and mixing height) to estimate monthly averages of ambient air pollution concentrations. The model is shown to have a wide range of applicability; it works equally well for a wide range of cities that have very different temporal CO distributions. The model is suited for assessing long-term trends in ambient air pollutants and can also be used for estimating seasonal variations in concentration, estimation of trends in emissions, and for filling in gaps in the ambient concentration record.