Urban Air Pollution Monitoring with DOAS Considering the Local Meteorological Situation

The concentrations of ozone, NO₂ and SO₂, measured with a DOAS system 70 m above ground level in the city of Graz were compared with data from conventional ground stations. The dependence of vertical trace-gas distributions on stability categories and time of the day or year was investigated. Concerning the maximum ozone concentrations in summer, the DOAS data are representative for the ground-level situation. In average, the concentrations 70 m above ground are more than twice the ground-level concentrations. It has been shown that beside the reaction with NO, dry deposition is an important sink for ozone near the surface. The DOAS NO₂-concentrations are representative for ground-level conditions in summer, except for the morning maximum of NO₂. In winter the DOAS NO₂-concentrations amount for 73% of the ground level values in average. Concerning the slow reacting trace gas SO₂, the DOAS data are always representative for the ground-level conditions.     

Mixed Integer Linear Programming Models for Selecting Ground-Level Ozone Control Strategies

The traditional strategy for ground-level ozone control is to apply emission reductions across the board throughout certain time periods and locations. In this paper, we study various mixed integer linear programming (MILP) models that seek to select targeted control strategies for the Dallas Fort-Worth (DFW) region to reduce emissions, in order to achieve the State Implementation Plan (SIP) requirements with minimum cost. Statistics and optimization methods are used to determine a potential set of cost-effective control strategies for reducing ozone. These targeted control strategies are specified for different types of emission sources in various time periods and locations. Three MILP models, a static model, a sequential model, and a dynamic model, are studied in this research. These different MILP models allow decision makers to study how the targeted control strategies change under different circumstances. Meanwhile, two types of auxiliary variables are considered as supplemental control strategies in the optimization if the current set of control strategies is unable to reduce ozone to comply with the 8-h ozone standard. Results from the different models can provide decision makers with information concerning how the effectiveness of the control strategies varies with daily emission patterns and meteorology.     

Grey Box and Component Models to Forecast Ozone Episodes: A Comparison Study

For the purpose of short-term forecasting of high ozone concentration episodes stochastic models have been suggested and developed in the literature. The present paper compares the quality of forecasts produced by a grey box and a component time-series model. The summer ozone patterns for three European urban areas (two continental and one mediterranean) are processed. By means of forecast performance indices according to EC and WHO guidelines, the following features of the models could be found: The grey box model is highly adaptive and produces forecasts with low error variance that increases with the time horizon of forecast. The component model is more 'stiff' that results in a higher forecast-error variance and poorer adaption in detail. The forecast horizon, however, could be enlarged with this model. The accuracy of predicting threshold exceedance is similar for both models. This can be understood from the assumption of a cyclical time development of ozone that was made for both models.     

Prediction of daily ground-level ozone concentration maxima over New Delhi

The pollution levels in New Delhi from industrial, residential, and transportation sources are continuously growing. As one of the major pollutants, ground-level ozone is responsible for various adverse effects on both humans and foliage. The present study aims to predict daily ground-level ozone concentration maxima over a site situated in New Delhi through neural networks (NN) and multiple-regression (MR) analysis. Although these methodologies are case and site specific, they are being developed and used widely. Therefore, to test these methodologies for New Delhi where no such study is available for ground-level ozone, six models have been developed based on NNs and MR using the same input data set. The changes in the performance capability of the two methods are sensitive to the selection of input parameters. The results are encouraging, and remarkable improvements in the performance of the models have been observed.     

欧盟臭氧污染监测现状及我国开展臭氧污染监测的建议

综述了欧盟臭氧前体物排放及地面臭氧污染监测的现状,分析了欧盟重视地面臭氧污染监测的原因及存在的主要问题.根据欧盟在地面臭氧污染监测方面的经验和做法,提出了加强我国地面臭氧污染监测的必要性和具体建议.     

内蒙古近地面臭氧污染时空分布特征及气象条件分析

使用2018—2020年内蒙古臭氧(O₃)、气象要素观测资料和NCEP FNL资料,统计分析内蒙古近地面O₃质量浓度的时空分布特征和变化趋势,并针对全区O₃污染典型个例分析其天气形势和气象要素的影响作用。结果表明:内蒙古2018—2020年O₃质量浓度年评价值呈逐年下降趋势,2020年较2018年下降10.3%,各盟市O₃超标率也显著降低,仅赤峰市和通辽市略微上升。内蒙古O₃质量浓度高值分布在中西部偏南地区,尤其是乌海市和鄂尔多斯市;O₃超标率峰值主要出现在5—7月,周末效应存在东西部差异。O₃浓度变化和天气形势关系密切,南部暖平流和暖高压控制有利于O₃生成,西北部冷平流和冷涡发展使得O₃浓度下降;高温、低湿、微风和较高的能见度均为诱发O₃污染的重要气象条件,而西北大风通过降低温度、能见度和易于扩散的风向使得内蒙古O₃浓度降低,但同时可能会导致PM₁₀污染。     

Ordinal time series model for forecasting air quality index for ozone in Southern California

Air quality index (AQI) for ozone is currently divided into six states depending on the level of public health concern. Generalized linear type modeling is a convenient and effective way to handle the AQI state, which can be characterized as non-stationary ordinal-valued time series. Various link functions which include cumulative logit, cumulative probit, and complimentary log-log are considered, and the partial maximum likelihood method is used for estimation. For a comparison purpose, the identity link, which yields a multiple regression model on the cumulative probabilities, is also considered. Random time-varying covariates include past AQI states, various meteorological processes, and periodic components. For model selection and comparison, the partial likelihood ratio tests, AIC and SIC are used. The proposed models are applied to 3 years of daily AQI ozone data from a station in San Bernardino County, CA. An independent year-long data from the same station are used to evaluate the performance of day-ahead forecasts of AQI state. The results show that the logit and probit models remove the non-stationarity in residuals, and both models successfully forecast day-ahead AQI states with almost 90 % of the chance.     

Forecasting Daily Maximum Ozone Concentrations in the Athens Basin

In the work ozone data from the Liossion monitoring station of the Athens/PERPA network are analysed. Data cover the months May to September for the period 1987–93. Four statistical models, three multiple regression and one ARIMA (0,1,2), for the prediction of the daily maximum 1-hour ozone concentrations are developed. All models together, with a persistence forecast, are evaluated and compared with the 1993's data, not used in the models development. Validation statistics were used to assess the relative accuracy of models. Analysis, concerning the models' ability to forecast real ozone episodes, was also carried out. Two of the three regression models provide the most accurate forecasts. The ARIMA model had the worst performance, even lower than the persistence one. The forecast skill of a bivariate wind speed and persistence based regression model for ozone episode days was found to be quite satisfactory, with a detection rate of 73% and 60% for O3 >180 g m-3 and O3 >200 g m-3, respectively.     

Estimation of Ozone Trend in Central Greece, Based on Meteorologically Adjusted Time Series

Data referring to an approximately 8-year period (1999–2007) are analyzed in order to estimate the trend of the daily maximum hourly value of ozone concentration at the east coast of central Greece, where the summer background ozone concentration is high. A Kolmogorov–Zurbenko filter is applied to remove the short-term component from the raw time series of ozone and meteorological variables. Regression models are developed in order to produce meteorologically adjusted ozone time series, involving the noise-free temperature, relative humidity, and wind speed as independent variables. The analysis verifies that the meteorological adjustment provides better results on estimating ozone’s trend, which is found to be increasing (α?=?0.001) with an annual rate of 1.34?±?0.07?μg/m³. This trend could mainly be attributed to policy and changes in the emissions of ozone’s precursors. Additionally, the short-term component of ozone concentration is also meteorologically adjusted and its impact on the trend is examined. The analysis shows that its contribution is of minor importance when the ozone trend is adjusted by temperature, relative humidity, and wind speed. Moreover, the sea breeze circulation system that is frequently developed in the area influences the short-term and seasonal ozone variation, and therefore, it should be taken into account when producing meteorologically adjusted time series. The study’s conclusions could be exploited by environmental and agricultural authorities in order to develop their long-term strategies towards the air quality management.     

大连市夏季近地面臭氧污染数值模拟和控制对策研究

通过区域空气质量模型CAMx对大连市2015年8月近地面臭氧(O_3)污染进行模拟,探讨了O_3及其生成前体物(NOx和VOCs)的来源,O_3生成控制区,并根据敏感性分析结果对前体物排放的控制效果进行了定量评估。结果表明:本地NOx排放对大连地区的NOx浓度贡献占90%以上,本地VOCs排放对大连地区的VOCs浓度贡献占80%以上,而本地NOx和VOCs排放对大连地区O_3浓度贡献仅占29%;大连市整体上为VOCs控制区,控制VOCs能有效降低O_3污染,还能有效削减O_3的峰值浓度;通过敏感性分析结果计算得出,削减大连本地工业源VOCs和民用源VOCs能够有效降低大连地区O_3浓度,削减10%的工业源VOCs能使市区O_3平均浓度降低2%左右,削减10%的民用源VOCs能使大连市区平均O_3浓度降低1%左右。建议NOx与VOCs削减比例为1∶2,对大连市O_3和PM2.5污染进行协同控制。     

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.     

Simulation and Evaluation of Control Strategies for Ozone Reduction in a Complex Terrain in Southwestern Spain

During the central months of the year, southwestern Spain is under strong insolation and weak synoptic forcing, promoting the development of sea breezes and mountain-induced winds and creating recirculations of pollutants. The complex topography of the Southwestern Iberia Peninsula induces the formation of vertical layers, into which the pollutants are injected and subjected to long-distance transport and compensatory subsidence. The characteristics of these highly complex flows have important effects on the pollutant dispersion. Air pollution studies in very complex terrains require high-resolution modelling for resolving the flow dynamics. This paper shows the results obtained from using the MM5-CAMx multiscale-nested air quality model to relate the sensitivity regimes for ozone, nitrogen oxides and volatile organic compounds in an area of high geographical complexity. The article assesses the impact on the hourly and eight-hourly maximum daily ozone concentrations of four reduction strategies during two ozone pollution episodes. This analysis of the ozone response has led to a preliminary evaluation of the effectiveness of the most common control strategies: traffic, industry, mixed traffic and industry, and closure of some of the largest industries (oil and petrochemical refineries). Photochemical indicators show that ozone chemistry in southwestern Spain is strongly sensitive to NO _x . However, volatile organic compound-sensitive points are found in areas with anthropogenic influence (highways, cities and industrial parks). Our results indicate that reductions in road traffic lead to ozone reductions over large areas, whereas reductions in industrial emissions, despite sometimes showing greater decreases in the maximum hourly and eight-hourly ground-level ozone levels, lead to ozone reductions in a local area only. In the control study case, with the oil refinery and the petrochemical plants closed, decreases in ozone hourly concentrations are up to 40% higher than in the other emission control scenarios studied. This analysis provides an assessment of the effectiveness of different policies for controlling precursor emissions by comparing the modelled results for different scenarios.     

Some Dependence Models for the Time Between Ozone Exceedances in Mexico City

In this paper, we consider several modelling approaches for the mean time between exceedances of a given environmental threshold. The interest here resides in the time between ozone exceedances (also called ozone inter-exceedances times). The proposed models assume two basic density functions for the time between surpassings: the Weibull and the generalised exponential functions. Considering those distributions, a random effect with autoregressive structure is taken into account to determine unexpected changes in the mean of the inter-exceedances density functions. Those unexpected changes could be captured either by their scale parameter or by both their scale and shape parameters. The models are applied to ozone data from the monitoring network of Mexico City. Selection of the model that best explains the data is performed using the deviance information criterion and also the sum of the absolute values of the differences between the estimated and observed means of the inter-exceedances times. An analysis to detect the possible presence of change-points is also presented.     

Establishment of a structural equation model for ground-level ozone: a case study at an urban roadside site

This study established a cause–effect relationship between ground-level ozone and latent variables employing partial least-squares analysis at an urban roadside site in four distinct seasons. Two multivariate analytic methods, factor analysis, and cluster analysis were adopted to cite and identify suitable latent variables from 14 observed variables (i.e., meteorological factors, wind and primary air pollutants) in 2008–2010. Analytical results showed that the first six components explained 80.3 % of the variance, and eigenvalues of the first four components were greater than 1. The effectiveness of this model was empirically confirmed with three indicators. Except for surface pressure, factor loadings of observed variables were 0.303–0.910 and reached statistical significance at the 5 % level. Composite reliabilities for latent variables were 0.672–0.812 and average variances were 0.404–0.547, except for latent variable “primary” in spring; thus, discriminant validity and convergent validity were marginally accepted. The developed model is suitable for the assessment of urban roadside surface ozone, considering interactions among meteorological factors, wind factors, and primary air pollutants in each season.     

A national ozone biomonitoring program--results from field surveys of ozone sensitive plants in northeastern forests (1994-2000)

Ozone biomonitoring is a detection and monitoring techniquethat involves documenting ozone-induced visible injury toknown ozone-sensitive species under conditions of ambientexposure. The USDA Forest Service administers a long-term,nationwide ozone biomonitoring program to address public andscientific concerns about ozone impacts on forest health. Asystematic grid is used as the basis for biomonitoring sitelocations. At each site, trained field crews evaluate amaximum of thirty plants of up to six species and record the amount and severity of leaf-injury on individualplants. Injury from ozone was found more often on biomonitoring sites in the eastern Unites States than in theinterior or west-coast areas. Further results from thenortheast reveal that in any year, there is a higherpercentage of ozone-injured plants with more severe symptomsin areas with relatively high ozone concentrations than inareas with relatively low ozone. In very dry years (e.g.,1999) the percentage of injured plants and injury severityestimates are both sharply reduced even though ambient ozoneexposures are high. These findings demonstrate thatbiomonitoring data provide meaningful evidence of when highozone concentrations during the growing season have biologicalsignificance. Any assessment of ozone stress in the forestenvironment must include both biomonitoring (i.e., plantresponse) and air quality data to be complete.     

Forecasting of daily total atmospheric ozone in Isfahan

A neural network combined to an artificial neural network model is used to forecast daily total atmospheric ozone over Isfahan city in Iran. In this work, in order to forecast the total column ozone over Isfahan, we have examined several neural networks algorithms with different meteorological predictors based on the ozone-meteorological relationships with previous day's ozone value. The meteorological predictors consist of temperatures (dry and dew point) and geopotential heights at standard levels of 100, 50, 30, 20 and 10 hPa with their wind speed and direction. These data together with previous day total ozone forms the input matrix of the neural model that is based on the back propagation algorithm (BPA) structure. The output matrix is the daily total atmospheric ozone. The model was build based on daily data from 1997 to 2004 obtained from Isfahan ozonometric station data. After modeling these data we used 3 year (from 2001 to 2003) of daily total ozone for testing the accuracy of model. In this experiment, with the final neural network, the total ozone are fairly well predicted, with an Agreement Index 76%.     

湖北臭氧分布特征及其管控措施

采用地面站点观测、卫星观测以及UWCM 0-D箱子模型模拟的方法研究湖北2013—2015年臭氧时空分布特征,并探讨其管控措施。从地面站点观测看出,时间分布上,这3年臭氧年平均浓度经历先下降后上升的过程,总体呈上升趋势,而二氧化氮年平均浓度则呈现持续下降的趋势;空间分布上,湖北各区域臭氧浓度分布不均匀,呈现东高西低的递减分布趋势。从卫星观测数据看出,2015年湖北的臭氧柱浓度高于2013、2014年同期。从空间分布来看,臭氧的柱浓度是从东北到西南、从省外到省内逐渐递减,因此推测,除了本地生成,湖北的臭氧有一部分是来源于省外传输。最大臭氧生成量法显示,烯烃(乙烯和丙烯)对湖北夏天臭氧生成量的贡献远大于其他挥发性有机化合物。箱子模型模拟的结果显示,湖北应该通过控制挥发性有机化合物的排放来降低臭氧生成速率,控制氮氧化物反而使臭氧生成速率提高。     

Ensemble Techniques to Improve Air Quality Assessment: Focus on O3 and PM

Five air quality models were applied over Portugal for July 2006 with an ensemble purpose. These models were used, with their own meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and its ensemble for ozone (O₃) and particulate matter was performed using monitoring data from 22 background stations over Portugal. After removing the bias from each model, different ensemble techniques were applied and compared. Besides the median, several weighted ensemble approaches were tested and intercompared: static (SLR) and dynamic (DLR) multiple linear regressions (using less-square optimization method) and the Bayesian Model Averaging (BMA) methodology. The goal of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results. The obtained results revealed that no one of the 4 tested ensembles clearly outperforms the others on the basis of statistical parameters and probabilistic analysis (reliability and resolution properties). Nevertheless, statistical results have shown that the application of the weights slightly improves ensemble performance when compared to those obtained from the median ensemble. The same statistical analysis together with the probabilistic measures demonstrates that the SLR and BMA methods are the best performers amongst the assessed methodologies.