Estimates of AOT ozone indices from time-integrated ozone data and hourly air temperature measurements in southwest Sweden

Surface ozone concentration and surface air temperature was measured hourly at three coastal sites, four low elevation inland sites and two high elevation inland sites in southwestern Sweden. Diurnal ozone concentration range (DOR) and diurnal temperature range (DTR) were strongly correlated, both spatially and temporally, most likely because both depended on atmospheric stability. Accumulated ozone exposure above a threshold concentration of x nmol mol⁻¹ (AOTx) was estimated from time-integrated ozone concentration (as from diffusive sampling) and measures of ozone concentration variability. Two methods both estimated 24-h AOTx with high accuracy (modelling efficiencies >90% for x ≤ 40 nmol mol⁻¹). Daytime (08:00–20:00) AOTx could not be equally well estimated. Estimates were better for lower AOT thresholds. Diffusive ozone concentration sampling, combined with hourly temperature monitoring, could be a valuable complement to ozone concentration monitoring with continuous instruments.     

A multi-variate statistical model integrating passive sampler and meteorology data to predict the frequency distributions of hourly ambient ozone (O3) concentrations

A multi-variate, non-linear statistical model is described to simulate passive O3 sampler data to mimic the hourly frequency distributions of continuous measurements using climatologic O3 indicators and passive sampler measurements. The main meteorological parameters identified by the model were, air temperature, relative humidity, solar radiation and wind speed, although other parameters were also considered. Together, air temperature, relative humidity and passive sampler data by themselves could explain 62.5-67.5% (R(2)) of the corresponding variability of the continuously measured O3 data. The final correlation coefficients (r) between the predicted hourly O3 concentrations from the passive sampler data and the true, continuous measurements were 0.819-0.854, with an accuracy of 92-94% for the predictive capability. With the addition of soil moisture data, the model can lead to the first order approximation of atmospheric O3 flux and plant stomatal uptake. Additionally, if such data are coupled to multi-point plant response measurements, meaningful cause-effect relationships can be derived in the future.     

Prediction of hourly O3 concentrations using support vector regression algorithms

In this paper we present an application of the Support Vector Regression algorithm (SVMr) to the prediction of hourly ozone values in Madrid urban area. In order to improve the training capacity of SVMrs, we have used a recently proposed approach, based on reductions of the SVMr hyper-parameters search space. Using the modified SVMr, we study different influences which may modify the ozone prediction, such as previous ozone measurements in a given station, measurements in neighbors stations, and the influence of meteorologic variables. We use statistical tests to verify the significance of incorporating different variables into the SVMr. A comparison with the results obtained using a neural network (multi-layer perceptron) is also carried out. This study has been carried out in 5 different stations of the air pollution monitoring network of Madrid, so the conclusions raised are backed by real data. The final result of the work is a robust and powerful software for tropospheric ozone prediction in Madrid. Also, the prediction tool based on SVMr is flexible enough to incorporate any other prediction variable, such as city models, or traffic patters, which may improve the prediction obtained with the SVMr.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Dynamic evaluation of a regional air quality model: Assessing the emissions-induced weekly ozone cycle

Air quality models are used to predict changes in pollutant concentrations resulting from envisioned emission control policies. Recognizing the need to assess the credibility of air quality models in a policy-relevant context, we perform a dynamic evaluation of the Community Multiscale Air Quality (CMAQ) modeling system for the “weekend ozone effect” to determine if observed changes in ozone due to weekday-to-weekend (WDWE) reductions in precursor emissions can be accurately simulated. The weekend ozone effect offers a unique opportunity for dynamic evaluation, as it is a widely documented phenomenon that has persisted since the 1970s. In many urban areas of the Unites States, higher ozone has been observed on weekends than weekdays, despite dramatically reduced emissions of ozone precursors (nitrogen oxides [NO_x] and volatile organic compounds [VOCs]) on weekends. More recent measurements, however, suggest shifts in the spatial extent or reductions in WDWE ozone differences. Using 18 years (1988–2005) of observed and modeled ozone and temperature data across the northeastern United States, we re-examine the long-term trends in the weekend effect and confounding factors that may be complicating the interpretation of this trend and explore whether CMAQ can replicate the temporal features of the observed weekend effect. The amplitudes of the weekly ozone cycle have decreased during the 18-year period in our study domain, but the year-to-year variability in weekend minus weekday (WEWD) ozone amplitudes is quite large. Inter-annual variability in meteorology appears to influence WEWD differences in ozone, as well as WEWD differences in VOC and NO_x emissions. Because of the large inter-annual variability, modeling strategies using a single episode lasting a few days or a few episodes in a given year may not capture the WEWD signal that exists over longer time periods. The CMAQ model showed skill in predicting the absolute values of ozone concentrations during the daytime. However, early morning NO_x concentrations were underestimated and ozone levels were overestimated. Also, the modeled response of ozone to WEWD differences in emissions was somewhat less than that observed. This study reveals that model performance may be improved by (1) properly estimating mobile source NO_x emissions and their temporal distributions, especially for diesel vehicles; (2) reducing the grid cell size in the lowest layer of CMAQ; and, (3) using time-dependent and more realistic boundary conditions for the CMAQ simulations.     

Integrated assessment modeling of atmospheric pollutants in the Southern Appalachian Mountains. Part I: hourly and seasonal ozone

Recently, a comprehensive air quality modeling system was developed as part of the Southern Appalachians Mountains Initiative (SAMI) with the ability to simulate meteorology, emissions, ozone, size- and composition-resolved particulate matter, and pollutant deposition fluxes. As part of SAMI, the RAMS/EMS-95/URM-1ATM modeling system was used to evaluate potential emission control strategies to reduce atmospheric pollutant levels at Class I areas located in the Southern Appalachians Mountains. This article discusses the details of the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. The daily mean normalized bias and error for 1-hr and 8-hr ozone were within U.S. Environment Protection Agency guidance criteria for urban-scale modeling. The model typically showed a systematic overestimation for low ozone levels and an underestimation for high levels. Because SAMI was primarily interested in simulating the growing season ozone levels in Class I areas, daily and seasonal cumulative ozone exposure, as characterized by the W126 index, were also evaluated. The daily ozone W126 performance was not as good as the hourly ozone performance; however, the seasonal ozone W126 scaled up from daily values was within 17% of the observations at two typical Class I areas of the SAMI region. The overall ozone performance of the model was deemed acceptable for the purposes of SAMI's assessment.     

Spatiotemporal modelling of ozone distribution in the State of California

This paper is concerned with the spatiotemporal mapping of monthly 8-h average ozone (O₃) concentrations over California during a 15-years period. The basic methodology of our analysis is based on the spatiotemporal random field (S/TRF) theory. We use a S/TRF decomposition model with a dominant seasonal O₃ component that may change significantly from site to site. O₃ seasonal patterns are estimated and separated from stochastic fluctuations. By means of Bayesian Maximum Entropy (BME) analysis, physically meaningful and sufficiently detailed space–time maps of the seasonal O₃ patterns are generated across space and time. During the summer and winter months the seasonal O₃ concentration maps exhibit clear and progressively changing geographical patterns over time, suggesting the existence of relationships in accordance with the typical physiographic and climatologic features of California. BME mapping accuracy can be superior to that of other techniques commonly used by EPA; its framework can rigorously assimilate useful data sources that were previously unaccounted for; the generated maps offer valuable assessments of the spatiotemporal O₃ patterns that can be helpful in the identification of physical mechanisms and their interrelations, the design of human exposure and population health models, and in risk assessment. As they focus on the seasonal patterns, the maps are not contingent on short-time and locally prevalent weather conditions, which are of no interest in a global and non-forecasting framework. Moreover, the maps offer valuable insight about the space–time O₃ concentration patterns and are, thus, helpful for disentangling the influence of explanatory factors or even for identifying some influential ones that could have been otherwise overlooked.     

Factors influencing the ground level distribution of ozone in Europe

Ozone is a widely distributed pollutant in the atmospheric boundary layer over north west Europe. Three main sources have been identified: the stratosphere, the free troposphere and boundary layer photochemical production. The pattern of ground level ozone concentrations resulting from these three sources cannot be accurately specified. Ozone shows significant variations in space and time but because of the high cost of continuous monitoring equipment, spatial variations on a national and international basis have not been studied in detail. Variations in ozone concentrations at individual monitoring sites have been given a great deal of attention and experience gained from United Kingdom monitoring sites is described in some detail. The averaging time statistical model of Larsen is employed to relate the exposure levels measured over different averaging periods. Diurnal variations have a major influence on exposure levels at sites nominally exposed to the same regional ozone distribution. The physical and chemical mechanisms which give rise to diurnal variations are detailed so that sites can be screened for different diurnal behaviour characteristics.     

Understanding the effectiveness of precursor reductions in lowering 8-hr ozone concentrations

Analyses of ambient measured ozone data were used in conjunction with the application of photochemical modeling to determine the technical feasibility of attaining the federal 8-hr ozone standard in central California. Various combinations of volatile organic compound (VOC) and oxides of nitrogen (NOx) emission reductions were effective in lowering modeled peak 1-hr ozone concentrations. However, VOC emissions reductions were found to have only a modest impact on modeled peak 8-hr ozone concentrations. NOx emission reductions generally lowered 8-hr ozone concentrations, but their effectiveness was partially or, in some cases, wholly offset by the increase in the number of NO cycles and, hence, in the ozone produced per NO. As a result, substantial NOx emission reductions--70 to 90%--were required to reduce peak 8-hr ozone concentrations to the level of the standard throughout the modeling domain. These modeling results provide a possible physical explanation for recent analyses that have reported more prominent trends in peak 1-hr ozone levels than in peak 8-hr ozone concentrations or in occurrences of mid-level (60-90 parts per billion by volume) ozone concentrations. The findings also have serious implications for the feasibility of attaining the 8-hr ozone standard in central California. Further efforts are needed to clarify the applicability of the modeling results to the full set of days with ozone levels exceeding the 8-hr ozone standard, as well as their applicability to other geographical areas.     

Preliminary background ozone concentrations in the mountain and coastal areas of Bulgaria

Urban and non-urban rural ozone (O3) concentrations are high in Bulgaria and often exceed the European AOT40 ecosystem as well as the AOT60 human health standards. This paper presents preliminary estimates to establish background, non-urban O3 concentrations for the southern region of Bulgaria. Ozone concentrations from three distinctly different sites are presented: a mountain site influenced by mountain-valley wind flow; a coastal site influenced by sea-breeze wind flow; and a 1700-m mountain peak site without 'local' wind flow characteristics. The latter offers the best estimate of 46-50 ppb for a background O3 level. The highest non-urban hourly value, 118 ppb, was measured at the mountain-valley site.     

Radial diffusive sampler for the determination of 8-h ambient ozone concentrations

The 8-h ozone radial diffusive sampler was evaluated according to the CEN protocol for the validation of diffusive samplers. All the parameters regarding the sampler characteristics were found to be consistent with the requirements of this protocol apart from the blank value, which must be evaluated and subtracted at each sampling. The nominal uptake rate was determined in laboratory conditions. However, the uptake rate depends on the mass uptake, temperature, humidity and on the combination of temperature and humidity. Based on laboratory experiments, an empirical model has been established which improved the agreement between the radial sampler and the reference method. This improvement was observed under several different meteorological and emission conditions of sampling. By using the model equation of uptake rate, the data quality objective of 30% for the expanded uncertainty included in the O(3) European Directive, is easily attained. Therefore, the sampler represents an appropriate indicative method.     

Prediction of ozone concentrations in Oporto city with statistical approaches

The performance of three statistical methods: time-series, multiple linear regression and feedforward artificial neural networks models were compared to predict the daily mean ozone concentrations. The study here reported was based on data from one urban site with traffic influences and one rural background site. The studies were performed for the year 2002 and the respective four trimesters separately. In the multiple linear regression and feedforward artificial neural network models, the concentrations of ozone, the concentrations of its precursors (nitrogen oxides) and some meteorological variables for one and two days before the prediction day were used as predictors. For the application of these models in the validation step, the inputs of ozone concentration for one and two days before were replaced by the ozone concentrations predicted by the models. The results showed that time-series modelling was not profitable. In the development step, similar performances were obtained with multiple linear regression and feedforward artificial neural network. Better performance indexes were achieved with feedforward artificial neural network models in validation step. Concluding, feedforward artificial neural network models were more efficient to predict ozone concentrations.     

Estimating changes in urban ozone concentrations due to life cycle emissions from hydrogen transportation systems

Hydrogen has been proposed as a low polluting alternative transportation fuel that could help improve urban air quality. This paper examines the potential impact of introducing a hydrogen-based transportation system on urban ambient ozone concentrations. This paper considers two scenarios, where significant numbers of new hydrogen vehicles are added to a constant number of gasoline vehicles. In our scenarios hydrogen fuel cell vehicles (HFCVs) are introduced in Sacramento, California at market penetrations of 9% and 20%. From a life cycle analysis (LCA) perspective, considering all the emissions involved in producing, transporting, and using hydrogen, this research compares three hypothetical natural gas to hydrogen pathways: (1) on-site hydrogen production; (2) central hydrogen production with pipeline delivery; and (3) central hydrogen production with liquid hydrogen truck delivery. Using a regression model, this research shows that the daily maximum temperature correlates well with atmospheric ozone formation. However, increases in initial VOC and NO_x concentrations do not necessarily increase the peak ozone concentration, and may even cause it to decrease. It is found that ozone formation is generally limited by NO_x in the summer and is mostly limited by VOC in the fall in Sacramento. Of the three hydrogen pathways, the truck delivery pathway contributes the most to ozone precursor emissions. Ozone precursor emissions from the truck pathway at 9% market penetration can cause additional 3-h average VOC (or NO_x) concentrations up to approximately 0.05% (or 1%) of current pollution levels, and at 20% market penetration up to approximately 0.1% (or 2%) of current pollution levels. However, all of the hydrogen pathways would result in very small (either negative or positive) changes in ozone air quality. In some cases they will result in worse ozone air quality (mostly in July, August, and September), and in some cases they will result in better ozone air quality (mostly in October). The truck pathway tends to cause a much wider fluctuation in degradation or improvement of ozone air quality: percentage changes in peak ozone concentrations are approximately −0.01% to 0.04% for the assumed 9% market penetration, and approximately −0.03% to 0.1% for the 20% market penetration. Moreover, the 20% on-site pathway occasionally results in a decrease of about −0.1% of baseline ozone pollution. Compared to the current ambient pollution level, all three hydrogen pathways are unlikely to cause a serious ozone problem for market penetration levels of HFCVs in the 9–20% range.     

Long-term changes in the tropospheric column ozone from the ozone soundings over Europe

The tropospheric column of ozone is analyzed from the measurements of the vertical profile of ozone by balloon-born ozonesondes. The data base includes ∼16,000 ozone profiles collected above six European stations—three of them have begun the ozonesoundings since 1970. We present a trend analysis (with data up to 2005) focusing on detection of the long-term tropospheric ozone variability over the European network. The ozone time series have been examined separately for each station and season (DJF, MAM, JJA, SON) using a flexible trend model. A trend component of the model is taken as a smooth curve without a priori defined shape. A large increase in the European tropospheric ozone since the beginning of the 1970s (net change of ∼10% in summers and ∼30% in winters) and a kind of stabilization in the early 1990s have been corroborated by the study. This pattern comes from the most extensive data set of ozonesoundings over Hohenpeissenberg and Payern. The decadal differences in the trend pattern between these and other European stations are disclosed. The results of a stepwise regression model using various characteristics of the ozone and temperature profiles as explanatory variables for the tropospheric column ozone (TCO₃) variations show that the ozone changes may be reconstructed using the ozone mixing ratio at 500 hPa, the thermal tropopause (TT) height, and the difference between ozonepause and TT heights. It appears that the last two factors induce 20–30% of the net TCO₃ change over Hohenpeissenberg in the 1970–2004 period.     

Evidence for large average concentrations of the nitrate radical (NO3) in Western Europe from the HANSA hydrocarbon database

The nitrate radical (NO₃) was first measured in the atmosphere in the 1970s and suggestions were made that it could play a major role in oxidising many unsaturated hydrocarbons, such as those emitted from the biosphere. Analysis of the hydrocarbon mix over the North Atlantic Ocean suggested subsequently that the influence of NO₃ radical chemistry at night was even more extensive, being on a par with hydroxyl radical chemistry at some times of the year.The paper presents a detailed analysis of an extensive database of many nonmethane hydrocarbons collected at various sites around the North Sea in the mid 1990s during the HANSA project. By comparing the relative rates of oxidation of iso and normal pentane with that of toluene and benzene it clearly shows that the efficiency of NO₃ radical chemistry and hydroxyl radical chemistry over northwest Europe are similar in springtime and predicts an average nighttime NO₃ concentration of the order of 350 pptv, assuming an annual average OH concentration of 0.6×10⁶ cm⁻³. This value is very dependant on the average emission ratios of the different hydrocarbons and values between 200 and 600 pptv are possible. It is much larger than direct measurements made in Europe at the surface, but is of the same magnitude as concentrations measured recently from aircraft in the boundary layer over the northeast USA, and previously in vertical profiles by remote sounding over Europe.A simple analytical expression can be derived to calculate the NO₃ concentration at night with the only variables being ozone and the loss rate of N₂O₅, either to the ground or to aerosol surfaces. The concentrations of NO₃ calculated in this manner are similar to those derived from the analysis of the HANSA hydrocarbon database for typical conditions expected over Europe, but they are very dependant on the efficiency of the aerosol sink for N₂O₅.It is shown that NO₃ oxidation of many unsaturated hydrocarbons can indeed be more efficient than OH oxidation, especially at times of the year outside the summer season. Direct evidence for hydrocarbon oxidation by NO₃ radicals is shown by a series of peroxy radical measurements where the nighttime concentrations can be significantly higher than daytime concentrations in polluted air on occasion. Also the winter/summer (W/S) ratios of many unsaturated hydrocarbons are much lower than those expected from their removal purely by hydroxyl radical chemistry.The consequences of these findings are profound especially as satellite measurements of NO₂, a major precursor to NO₃, suggest that these high average concentrations of several hundred pptv could be widespread over most of the continents. This needs to be confirmed by direct in-situ measurement of nitrate radicals but it suggests a much larger role for NO₃ chemistry in the oxidation capacity of the atmosphere than realised hitherto.     

Short-term, real-time forecasting of hourly ozone, NO2 and NO levels by means of multiple linear regression modelling

Conclusions  The results are at least as good as those obtained with much more sophisticated models. Furthermore, the models can be easily run on a simple PC. This approach may provide a good diagnostic network (as seen in Bilbao) with real-time, short-term prognostic capabilities for a given location. The models have been built for Bilbao and are applicable only in this location. However, using the same methodology, similar results could be obtained in other environments if a good diagnostic network is available. The full paper is published in the Internet journal ‘Gate to EHS (Environmental and Health Science)’, in June 2001, and can be called up and cited by way of the DOI:http://dx.doi.org/10.1065/ehs2001.06.009     

On the local and regional influence on ground-level ozone concentrations in Hong Kong

Hong Kong is a densely populated city situated in the fast developing Pearl River Delta of southern China. In this study, the recent data on ozone (O3) and related air pollutants obtained at three sites in Hong Kong are analyzed to show the variations of O3 in urban, sub-urban and rural areas and the possible regional influences. Highest monthly averaged O3 was found at a northeastern rural site and lowest O3 level was observed at an urban site. The levels of NOx, CO, SO2 and PM10 showed a different spatial pattern with the highest level in the urban site and lowest at the rural site. Analysis of chemical species ratios such as SO2/NOx and CO/NOx indicated that the sites were under the influences of local and regional emissions to varying extents reflecting the characteristics of emission sources surround the respective sites. Seasonal pattern of O3 is examined. Low O3 level was found in summer and elevated levels occurred in autumn and spring. The latter appears different from the previous result obtained in 1996 indicating a single maximum occurring in autumn. Principal component analysis was used to further elucidate the relationships of air pollutants at each site. As expected, the O3 variation in the northeastern rural area was largely determined by regional chemical and transport processes, while the O3 variability at the southwestern suburban and urban sites were more influenced by local emissions. Despite the large difference in O3 levels across the sites, total potential ozone (O3+NO2) showed little variability. Cases of high O3 episodes were presented and elevated O3 levels were formed under the influence of tropical cyclone bringing in conditions of intense sunlight, high temperature and light winds. Elevated O3 levels were also found to correlate with enhanced ratio of SO2 to NOx, suggesting influence of regional emissions from the adjacent Pearl River Delta region.