Prediction of daily averaged PM10 concentrations by statistical time-varying model

In this study, a time-varying statistical model, TVAREX, was proposed for daily averaged PM₁₀ concentrations forecasting of coastal cities. It is a Kalman filter based autoregressive model with exogenous inputs depending on selected meteorological properties on the day of prediction. The TVAREX model was evaluated and compared to an ANN model, trained with the Levenberg–Marquardt backpropagation algorithm subjected to the same set of inputs. It was found that the error statistics of the TVAREX model in general were comparable to those of the ANN model, but the TVAREX model was more efficient in capturing the PM₁₀ pollution episodes due to its online nature, therefore having an appealing advantage for implementation.     

Reduction of net photosynthesis in oats after treatment with low concentrations of ozone

Oats (Avena sativa L. cv Titus) were exposed to low concentrations of O3 in an assimilation chamber system. Net photosynthesis (net CO2 uptake), measured before and after O3 fumigation, showed significantly different responses for leaves of different age. The oldest active leaf was the most sensitive to O3. Net photosynthesis was depressed after 2 h with 0.075 ppm (150 microg m(-3)) O3. For leaves exposed to 0.150 ppm (300 microg m(-3)) O3 for 2 h, net photosynthesis was reduced significantly for 4 h, after which recovery occurred, nearly reaching the preexposure level 19 h after the exposure. Dark respiration was initially more than doubled after exposure to 0.130 ppm (260 microg m(-3)) O3. There was no visible injury after any of the experiments. The results indicate that O3 may cause crop losses through effects on photosynthesis even in Scandinavia, where a typical O3 episode lasts 1 to 2 h, and the concentration seldom exceeds 0.150 ppm.     

The potential of statistical state space models in urban ozone forecasting

State space models for tropospheric urban ozone prediction are introduced and compared with linear regression models. The linear and non-linear state space models make accurate short-term predictions of the ozone dynamics. The average prediction error one hour in advance is 7 μg/m³ and increases logarithmically with time until it reaches 26 μg/m³ after 30 days. For a given sequence of solar radiation inputs, predictions converge exponentially with a time scale of 8 hours, so that the model is insensitive to perturbations of more than 150 μg/m³ O₃. The slow increase of the prediction error in addition to the uniqueness of the prediction are encouraging for applications of state space models in forecasting ozone levels when coupled with a model that predicts total radiation. Since a radiation prediction model will be more accurate during cloud-free conditions, in addition to the fact that the state space models perform better during the summer months, state space models are suitable for applications in sunny environments.     

Variability of ozone air quality indicators in selected metropolitan statistical areas

This article analyzes numerical variability in ozone air quality data to understand how this variability affects the number of violations seen each year in metropolitan statistical areas (MSAs). Three commonly cited violation indices are used: 1) the annual number of expected exceedances averaged over 3 years is greater than 1; 2) the n+ 1th hourly value in n years of data is greater than 0.12 ppm; and 3) the annual number of expected exceedances is greater than 1. Only the first index is consistent with applicable regulations. The analyses indicate that about 23 percent of all MSAs with valid data had one or more change in their ozone violation status between 1979 and 1987. This change in status occurred for approximately 7 percent of all MSA-years of available data. This statistic was about one-third of the value usually obtained when the two incorrect, but commonly used, criteria of ozone violations are used.     

High ozone concentrations at night in Jerusalem and Tel-Aviv

Elevated ozone concentrations, in excess of accepted background levels, were frequently measured at night in Jerusalem and Tel-Aviv. High ozone at night is connected with persisting low level inversions, under which the photochemically generated ozone is trapped during the day. With the gradual descent of the inversion towards evening and cessation of traffic, ozone levels rise, reaching peak values about 4 h after midnight. The onset of early morning traffic with increasing NO and other pollutants then decreases the ozone concentrations, which reach a minimum level at about 7–8 h local time.     

Urban and rural ozone concentrations in Alberta,Canada

Ozone concentrations in Alberta cities typically exhibit a maximum in May (up to 35 ppb) and a minimum in November (as low as 4 ppb). This behaviour is similar to that of rural Alberta O₃ concentrations. Annual O₃ concentrations at six urban monitoring stations vary from 11 ppb to 22 ppb and are about one-half the values at rural stations. In winter, urban O₃ concentrations are always smaller than rural concentrations and the cities act as sinks for O₃. Although urban stations do not exceed Canada's maximum acceptable levels of daily (25 ppb) and annual (15 ppb) O₃ concentrations as often as rural stations, the frequency is still quite large. Canada's hourly maximum desirable level (50 ppb) is exceeded 11 times more often at the remote (rural) station than at the downtown (urban) stations.     

Variation of ozone concentrations in the ambient air of Baghdad

Measurements of ozone concentration in the ambient air of the city of Baghdad were carried out for the period October 1983 to October 1984. The O₃, probably of local origin, showed a typical diurnal and seasonal variation. Maximum daily 1-h O₃ concentrations higher than the international ambient air quality standards were observed regularly during the summer months. High O₃ concentrations during the night were also observed. Scatter diagrams were used to relate the O₃ concentrations with temperature, solar radiation and humidity.     

Artificial neural network-derived trends in daily maximum surface ozone concentrations

Interannual variability in meteorological conditions can confound attempts to identify changes in ozone concentrations driven by reduced precursor emissions. In this paper, a technique is described that attempts to maximize the removal of meteorological variability from a daily maximum ozone time series, thereby revealing longer term changes in ozone concentrations with increased confidence. The technique employs artificial neural network [multilayer perceptron (MLP)] models, and is shown to remove more of the meteorological variability from U.S. ozone data than does a Kolmogorov-Zurbenko (KZ) filter and conventional regression-based technique.     

Prediction of ozone concentration in ambient air using multivariate methods

Multivariate statistical methods including pattern recognition (Principal Component Analysis--PCA) and modeling (Multiple Linear Regression--MLR, Partial Least Squares--PLS, as well as Principal Component Regression--PCR) methods were carried out to evaluate the state of ambient air in Miskolc (second largest city in Hungary). Samples were taken from near the ground at a place with an extremely heavy traffic. Although PCA is not able to determine the significance of variables, it can uncover their similarities and classify the cases. PCA revealed that it is worth to separate day and night data because different factors influence the ozone concentrations during all day. Ozone concentration was modeled by MLR and PCR with the same efficiency if the conditions of meteorological parameters were not changed (i.e. morning and afternoon). Without night data, PCR and PLS suggest that the main process is not a photochemical but a chemical one.     

Modeling the long-term frequency distribution of regional ozone concentrations

Efficient methods are developed for modeling emissions – air quality relationships that govern ozone and NO₂ concentrations over very long periods of time. A baseline model evaluation study is conducted to assess the accuracy and speed with which the relationship between pollutant emissions and the frequency distribution of O₃ concentrations throughout the year can be computed along with annual average NO₂ values using a deterministic photochemical airshed model driven by automated objective analysis of measured meteorological parameters. Methods developed are illustrated by application to the air quality situation that exists in Southern California. Model performance statistics for O₃ are similar to the results obtained in previous short-term episodic model evaluation studies that were based on hand-crafted meteorological inputs that are supplemented by expensive field measurement campaigns. Model predictions at one of the highest NO₂ concentration sites in the US indicate that measured violation of the US annual average NO₂ air quality standard at that site occurs because other species such as HNO₃ and PAN are measured as if they were NO₂ by the chemiluminescent NO_x monitors in current use.     

Contribution of anthropogenic pollutants to the increase of tropospheric ozone levels in the Oporto Metropolitan Area, Portugal since the 19th century

The main purpose of this study was to evaluate the contribution of anthropogenic pollutants to the increase of tropospheric ozone levels in the Oporto Metropolitan Area (Portugal) since the 19th century. The study was based on pre-industrial and recent data series, the results being analyzed according to the atmospheric chemistry. The treatment of ozone and meteorological data was performed by classical statistics and by time-series analysis. It was concluded that in the 19th century the ozone present in the troposphere was not of photochemical origin, being possible to consider the respective concentrations as reference values. For recent data a cycle of 8h for ozone concentrations could be related to traffic. Compared to the 19th century, the current concentrations were 147% higher (252% higher in May) due to the increased photochemical production associated with the increased anthropogenic emissions.     

Trends in near-surface ozone concentrations in Switzerland: the 1990s

A time series analysis of ozone monitoring data from several locations in Switzerland from 1991 to 1999 is presented. Different methods are used to address changes in the ozone level during these years and to account for the influence of changing meteorological conditions. The results show a slight decrease of the peaks but a highly significant increase of the mean value of around 0.5–0.9 ppb yr⁻¹. The frequency distribution has changed in the sense that very low values have become less frequent and that there is a strong increase in frequency of occurrence of half-hourly mean values between about 45 and 55 ppb. A selection procedure reveals slight tendencies towards different trends of afternoon ozone peaks in summer depending on weather and pollution situations. Ozone peaks tend to decrease on fair weather days at rural sites (but increase at urban sites) and show a small increase on cloudy and windy days. A non-linear regression model is used to estimate trends of summertime afternoon ozone peaks in the presence of meteorological variability. In the model, the long-term signal is additively split into a linear part and a part which is modulated by global radiation. The coefficients for both terms are statistically significant at many sites, with an increasing linear trend at the sites north of the Alps of around 1 ppb yr⁻¹ and a decrease of ozone peaks under fair weather conditions relative to cloudy conditions. When additionally considering the effect of precursor concentrations in the regression models, both trends are weakened, which means that they can partly be explained by changes in local to regional emissions. However, at the sites north of the Alps remains a tendency towards a positive linear “base trend” of around 0.4 ppb yr⁻¹. This could possibly be due to increasing background ozone concentrations.     

Prediction of the vertical profile of ozone based on ground-level ozone observations and cloud cover

A number of statistical techniques have been used to develop models to predict high-elevation ozone (O3) concentrations for each discrete hour of day as a function of elevation based on ground-level O3 observations. The analyses evaluated the effect of exclusion/inclusion of cloud cover as a variable. It was found that a simple model, using the current maximum ground-level O3 concentration and no effect of cloud cover provided a reasonable prediction of the vertical profile of O3, based on data analyzed from O3 sites located in North Carolina and Tennessee. The simple model provided an approach that estimates the concentration of O3 as a function of elevation (up to 1800 m) based on the statistical results with a +/- 13.5 ppb prediction error, an R2 of 0.56, and an index of agreement, d1, of 0.66. The inclusion of cloud cover resulted in a slight improvement in the model over the simple regression model. The developed models, which consist of two matrices of 24 equations (one for each hour of day for clear to partly cloudy conditions and one for cloudy conditions), can be used to estimate the vertical O3 profile based on the inputs of the current day's 1-hr maximum ground-level O3 concentration and the level of cloud cover.     

Spatial variations of ground level ozone concentrations in areas of different scales

The spatial variation of ground level ozone concentrations was investigated for areas of three different scales: (1) an air quality management district (a region about 100×70 km²) in northern Taiwan, (2) the neighborhood (about 2 km in radius) of an air quality monitoring station, and (3) an open field (about 400×600 m²) surrounded by 3- and 4-story buildings in an elementary school. Analysis of data on hourly ozone concentration, obtained at 13 m above the ground at 21 monitoring stations in the air quality management district, showed that the stations downwind of the urban center in the district had significantly higher ozone concentrations. Measurements for 8-h average ozone concentrations at 1.5 m above the ground by passive samplers showed that, in a flat area about 2 km in radius, the ratios of the ozone concentration at open areas to that at the monitoring station (0.86–0.93) were significantly higher than those obtained at areas with higher traffic flow and density of buildings (0.60–0.68). For the open field in an elementary school, the 8-h average ozone concentrations at 1.5 m above the ground at sites less than 10 m from the nearest building were considerably lower than those at sites farther away from buildings. The results indicated that, in areas of small scales, the spatial distributions of ozone concentration were highly non-uniform and there were appreciable day-to-day variability in spatial distribution. Such variability should be taken into account in determining the extent to which an individual is exposed to ozone.     

The influence of the photolysis rates on modelled ozone concentrations

The sensitivity of ozone formation in a photochemical model to the photolysis rates has been studied using two photolytic schemes (old EMEP and UKPTM). The results from the comparison of these rates illustrate how the different parameterisation may influence the ozone development. The implementation of the UKPTM photolysis rates into the EMEP chemical scheme resulted in lower ozone levels than those predicted using the old EMEP photolytic scheme.     

Vapor-phase concentrations of PAHs and their derivatives determined in a large city: Correlations with their atmospheric aerosol concentrations

Thirteen PAHs, five nitro-PAHs and two hydroxy-PAHs were determined in 55 vapor-phase samples collected in a suburban area of a large city (Madrid, Spain), from January 2008 to February 2009. The data obtained revealed correlations between the concentrations of these compounds and a series of meteorological factors (e.g., temperature, atmospheric pressure) and physical–chemical factors (e.g., nitrogen and sulfur oxides). As a consequence, seasonal trends were observed in the atmospheric pollutants. A “mean sample” for the 14-month period would contain a total PAH concentration of 13 835 ± 1625 pg m⁻³ and 122 ± 17 pg m⁻³ of nitro-PAHs. When the data were stratified by season, it emerged that a representative sample of the coldest months would contain 18 900 ± 2140 pg m⁻³ of PAHs and 150 ± 97 pg m⁻³ of nitro-PAHs, while in an average sample collected in the warmest months, these values drop to 9293 ± 1178 pg m⁻³ for the PAHs and to 97 ± 13 pg m⁻³ for the nitro-PAHs. Total vapor phase concentrations of PAHs were one order of magnitude higher than concentrations detected in atmospheric aerosol samples collected on the same dates. Total nitro-PAH concentrations were comparable to their aerosol concentrations whereas vapor phase OH-PAHs were below their limits of the detection, indicating these were trapped in airborne particles.     

Meteorologically adjusted trends of daily maximum ozone concentrations in Taipei,Taiwan

The meteorological conditions exert large impacts on ozone concentrations, and may mask the long-term trends in ozone concentrations resulting from precursor emissions. Estimation of long-term trends of ozone concentrations due to the changes in precursor emissions is important for corresponding control strategy. Multiple linear regression (method I), multilayer perceptron (MLP) neural network (method II) and Komogorov-Zurbenko (KZ) filter method plus MLP methodology (method III), are used to estimate the meteorologically adjusted long-term trends of daily maximum ozone concentrations by removing the masking effects of meteorological conditions in this study. The daily maximum ozone concentrations and relative meteorological variables were extracted from six air-monitoring stations in Taipei area from 1994 to 2001. The data collected during 1994–2000 period were used as modeling set and utilized to estimate the meteorologically adjusted trends, and the data of 2001 were used as the validation data. The meteorologically adjusted trends of ozone for these three methods were calculated and compared. The results show that both MLP and KZ filter +MLP models are more suitable than multiple linear regression for estimating the long-term trends of ozone in Taipei, Taiwan. The long-term linear trends of meteorologically adjusted ozone concentrations due to the precursor emissions show an increase trend at all stations, and the percent changes per year range from 1.0% to 2.25% during the modeling period in Taipei area.     

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.