Neural network-based study for predicting ground-level ozone concentration in large urban areas, applied to the Sao Paulo metropolitan area

Events of high concentration of ground-level ozone constitute a matter of major concern in large urban areas in terms of air quality, and public health. In the Sao Paulo Metropolitan Area (SPMA), air quality data generated by a network of air quality measuring stations have been used in a number of studies correlating ozone formation with different variables. A study was carried out on the application of neural network models in the identification of typical sceneries leading to high ground-level ozone concentrations in the SPMA. The results were then applied in the selection of variables, and in the definition of neural network-based models for estimating ozone levels from meteorological variables. When combined with existing weather prediction tools, the models can be applied in the prediction of ozone levels in the SPMA     

Characteristics of urban ground-level ozone in Korea

This study considers the characteristics of ground-level ozone (O3) in five Korean cities over a time period of 6-8 years. The focus of this study is daily maximum 1-hr and 8-hr concentrations. For all the study cities in the period examined, the mean and most of the percentiles (5, 10, 25, 50, 75, 90, and 95) for the daily maximum 1-hr and 8-hr concentrations showed increasing trends, although not all trends were statistically significant. The daily maximum 1-hr and 8-hr concentrations slowly increased during late winter, and peaks were attained during the summer season (from May to September). All the selected cities exhibited a high degree of correlation between their daily maximum 8-hr and 1-hr concentrations. The daily maximum 8-hr concentrations, which were climatologically equivalent to the Korean 1 hr/100 parts per billion (ppb) standard, were higher than the current 8 hr/60 ppb by a difference of 8-16 ppb. Compared with other cities in Korea, Seoul recorded a substantially higher frequency of days and hours with concentrations above 1 hr/100 ppb, and a higher frequency of days with concentrations above 8 hr/60 ppb and 8 hr/80 ppb. Seoul also recorded a substantially higher frequency of hours with concentrations above 1 hr/100 ppb than days with concentrations above 1 hr/100 ppb, implying that on some days severe exceedances persisted for more than one hour per day. During multiple-day episodes a North Pacific High dominated Korea, which is quite typical in Korea during the summer season.     

Comparison of ozone temporal scales for large urban, small urban, and rural areas in Georgia

Ground-level ozone (O3) time series are characterized by the sum of several distinct temporal scales: long-term, seasonal, synoptic, diurnal (daily), and intraday variation. In this study, the authors use a Kolmorogov-Zurbenko filter to separate the 1981-2001 O3 time-series from many sites in and around Georgia into these various components. The authors compare the temporal components to examine differences between small and large metropolitan areas and between urban and rural areas. They then focus on the synoptic component to define a predominant transport region or airshed for each site.     

Establishment of a Box-Jenkins multivariate time-series model to simulate ground-level peak daily one-hour ozone concentrations at Ta-Liao in Taiwan

Box-Jenkins univariate autoregressive integrated moving average (ARIMA) and regression with time-series error (RTSE) models were established to simulate historical peak daily 1-hr ozone concentrations at Ta-Liao, Taiwan, 1997-2001. During 1995-2003, the 600 days of Pollution Standard Index (PSI) more than 100 (peak daily 1-hr ozone concentrations detected by greater than 120 ppm) at Tao-Liao showed the highest ozone exceedances among the six monitoring stations in Kaohsiung County. To improve the predictability of extremely high ozone, two different principal components, PC1 and PC(1 + 2), were introduced in the RTSE model. Four typical predictors (particular matter with an aerodynamic diameter less than or equal to 10 microm, temperature, wind speed, and wind direction) plus a PC trigger remained significant in the RTSE model. The model performance statistics concluded that the RTSE model with PC1 was optimal, compared with the univariate ARIMA, the RTSE model without PC, and RTSE model with PC(1 + 2). The contingency table shows that the successful predictions of the univariate model were only 12.9% of that of the RTSE model with PC1. Also, the POD value was improved approximately 5-fold when the univariate model was replaced by the RTSE model, and almost 8-fold when it was replaced by the RTSE model with PC1. Moreover, introducing the PC trigger indeed enhanced the ozone predictability. After the PC trigger was introduced in the RTSE model, the POD was increased 69.9%, and the FAR was reduced 8.3%. The overall correlation between the observed and simulated ozone was improved 9.6%. Also, the first principal component was more useful than the first two components in playing the "trigger" role, though it counted only for 58.62% of the environmental variance during the high ozone days.     

Trends in concentration of ground-level ozone and meteorological conditions during high ozone episodes in the Kao-Ping Airshed, Taiwan

This work analyzes the variations in daily maximum 1-hr ozone (O3) concentrations and the long-term trends in annual means of hourly ambient concentrations of O3, nitrogen oxides (nitrous oxide + nitrogen dioxide), and nonmethane hydrocarbons in the three administrative regions of Kao-Ping airshed in southern Taiwan over a recent 8-yr period. The annual or monthly means of all maxima, most 95th percentiles, and some 90th percentiles of the daily maximum 1-hr O3 concentrations exceed the daily limit of 120 parts per billion by volume in all three regions, namely, Kao-hsiung City, Kso-hsiung County, and P'ing-tung County. The monthly means of daily maximum 1-hr O3 concentrations exhibit distinct seasonal variations, with a bimodal form with the maxima in autumn and late winter to the middle of spring and a minimum in summer. The long-term variations in the annual means of hourly O3 concentrations in the three regions exhibit increasing trends. These increases in O3 are associated with the decline in ambient concentrations of nitrogen oxides and nonmethane hydrocarbons. High O3 episodes occur most often in autumn and most rarely in summer. The seasonal mean mixing heights in descending order follow the order of spring, summer, autumn, and winter. Meteorological parameters in autumn and winter indicate that the ground-level O3 tends to accumulate and trigger a high O3 episode on a warm day with sufficient sunlight and low wind in a high-pressure system, consistent with the low mixing heights in these two seasons.     

A Lagrangian stochastic model for predicting concentration fluctuations in urban areas

A combined Lagrangian stochastic model with a micromixing sub-model is used to estimate the fluctuating concentrations observed in two wind tunnel experiments. The Lagrangian stochastic model allows fluid trajectories to be simulated in the inhomogeneous flow, while the mixing model accounts for the dissipation of fluctuations using the interaction by exchange with the mean (IEM) mechanism. The model is first tested against the open terrain, wind tunnel data of Fackrell, J.E. and Robins, A.E. [1982. Concentration fluctuations and fluxes in plumes from point sources in a turbulent boundary layer. Journal of Fluid Mechanics 117, 1–26] and shows good agreement with the observed mean concentrations and fluctuation intensities. The model is then compared with the wind tunnel simulation of a two-dimensional street canyon by Pavageau, M. and Schatzmann, M. [1999. Wind tunnel measurements of concentration fluctuations in an urban street canyon. Atmospheric Environment 33, 3961–3971]. Despite the limitations of the k–ε turbulence scheme and the IEM mixing mechanism, the model reproduces the fluctuation intensity pattern within the canyon well. Overall, the comparison with both sets of wind tunnel experiments are encouraging, and the simplicity of the model means that predictions in a complex, three-dimensional geometry can be produced in a practicable amount of time.     

Estimating long-term population exposure to ozone in urban areas of Europe

Tropospheric ozone concentrations regarded as harmful for human health are frequently encountered in Central Europe in summertime. Although ozone formation generally results from precursors transported over long distances, in urban areas local effects, such as reactions due to nearby emission sources, play a major role in determining ozone concentrations. Europe-wide mapping and modeling of population exposure to high ozone concentrations is subject to many uncertainties, because small-scale phenomena in urban areas can significantly change ozone levels from those of the surroundings. Currently the integrated assessment modeling of European ozone control strategies is done utilizing the results of large-scale models intended for estimating the rural background ozone levels. This paper presents an initial study on how much local nitrogen oxide (NOx) concentrations can explain variations between large-scale ozone model results and urban ozone measurements, on one hand, and between urban and nearby rural measurements, on the other. The impact of urban NOx concentrations on ozone levels was derived from chemical equations describing the ozone balance. The study investigated the applicability of the method for improving the accuracy of modeled population exposure, which is needed for efficient control strategy development. The method was tested with NOx and ozone measurements from both urban and rural areas in Switzerland and with the ozone predictions of the large-scale photochemical model currently used in designing Europe-wide control strategies for ground-level ozone. The results suggest that urban NOx levels are a significant explanatory factor in differences between urban and nearby rural ozone concentrations and that the phenomenon could be satisfactorily represented with this kind of method. Further research efforts should comprise testing of the method in more locations and analyzing the performance of more widely applicable ways of deriving the initial parameters.     

The effects of meteorology on ozone in urban areas and their use in assessing ozone trends

The United States Environmental Protection Agency issues periodic reports that describe air quality trends in the US. For some pollutants, such as ozone, both observed and meteorologically adjusted trends are displayed. This paper describes an improved statistical methodology for meteorologically adjusting ozone trends as well as characterizes the relationships between individual meteorological parameters and ozone. A generalized linear model that accommodates the nonlinear effects of the meteorological variables was fit to data collected for 39 major eastern US urban areas. Overall, the model performs very well, yielding R² statistics as high as 0.80. The analysis confirms that ozone is generally increasing with increasing temperature and decreasing with increasing relative humidity. Examination of the spatial gradients of these responses show that the effect of temperature on ozone is most pronounced in the north while the opposite is true of relative humidity. By including HYSPLIT-derived transport wind direction and distance in the model, it is shown that the largest incremental impact of wind direction on ozone occurs along the periphery of the study domain, which encompasses major NO_x emission sources.     

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.     

Storm water runoff concentration matrix for urban areas

The infrastructure (roads, sidewalk, commercial and residential structures) added during the land development and urbanisation process is designed to collect precipitation and convey it out of the watershed, typically in existing surface water channels, such as streams and rivers. The quality of surface water, seepage water and ground water is influenced by pollutants that collect on impervious surfaces and that are carried by urban storm water runoff. Heavy metals, e.g. lead (Pb), zinc (Zn), copper (Cu), cadmium (Cd), polycyclic aromatic hydrocarbons (PAH), mineral oil hydrocarbons (MOH) and readily soluble salts in runoff, contribute to the degradation of water. An intensive literature search on the distribution and concentration of the surface-dependent runoff water has been compiled. Concentration variations of several pollutants derived from different surfaces have been averaged. More than 300 references providing about 1300 data for different pollutants culminate in a representative concentration matrix consisting of medians and extreme values. This matrix can be applied to long-term valuations and numerical modelling of storm water treatment facilities.     

Surface ozone comparison conducted in two rural areas in central-southern Spain

Purpose  

The purpose of this work is to contribute to the understanding of the photochemical air pollution analysing the levels and temporal variations of surface ozone in two rural areas situated in central-southern Spain.     

Nitrogen oxides and ozone in Portugal: trends and ozone estimation in an urban and a rural site

Environmental Science and Pollution Research - This study provides an analysis of the spatial distribution and trends of NO, NO2 and O3 concentrations in Portugal between 1995 and 2010....     

Ground-based remote sensing measurements of aerosol and ozone in an urban area: A case study of mixing height evolution and its effect on ground-level ozone concentrations

We have estimated the mixing height (MH) and investigated the relationship between vertical mixing and ground-level ozone concentrations in Seoul, Korea, by using three ground-based active remote sensing instruments operating side by side: micro-pulse lidar (MPL), differential absorption lidar (DIAL), and differential optical absorption spectroscopy (DOAS). The MH is estimated from MPL measurements of aerosol extinction profiles by the gradient method under convective conditions. Comparisons of the MHs estimated from MPL and radiosonde measurements show a good agreement (r²=0.99). Continuous MPL measurements with high temporal and vertical resolution reveal the diurnal variations of the MH under convective conditions and the presence of a residual layer during the nighttime. Comprehensive measurements of ozone and aerosol by MPL, DIAL and DOAS during an high ozone episode (24–26 May 2000) in Seoul, Korea, reveal that (1) photochemical ozone production and advection from upwind regions (the western part of Seoul) contribute two peaks of ozone concentrations at the ground around 14:00 and 18:00 local time on 25 May 2000, respectively, and (2) the entrainment and the fumigation processes of ozone aloft in the nighttime residual layer into the ground is a major contributor of high concentrations of ground-level ozone observed on the following day (26 May 2000).     

A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area

Prediction of ambient ozone concentrations in urban areas would allow evaluation of such factors as compliance and noncompliance with EPA requirements. Though ozone prediction models exist, there is still a need for more accurate models. Development of these models is difficult because the meteorological variables and photochemical reactions involved in ozone formation are complex. In this study, we developed a neural network model for forecasting daily maximum ozone levels. We then compared the neural network's performance with those of two traditional statistical models, regression, and Box-Jenkins ARIMA. The neural network model for forecasting daily maximum ozone levels is different from the two statistical models because it employs a pattern recognition approach. Such an approach does not require specification of the structural form of the model. The results show that the neural network model is superior to the regression and Box-Jenkins ARIMA models we tested.     

Nocturnal depletion of photochemical ozone at a rural site

Nocturnal depletion of ozone during a period of photochemical pollution has been observed at a rural site by continuous ground-level measurements and the determination of vertical profiles of ozone concentration. An acoustic sounder was used to provide continuous information upon the vertical temperature structure of the atmosphere. The results are interpreted in terms of recent theories of long-range transport of photochemical ozone and the chemical and physical processes causing ozone depletion.     

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.     

Accuracy and reliability of an automated air quality forecast system for ozone in seven Kentucky metropolitan areas

An automated forecast system for ozone in seven Kentucky metropolitan areas has been operational since 2004. The forecast system automatically downloads the required input data twice each day, produces next-day forecasts of metro area peak 8-h average ozone concentration using a computer coded hybrid nonlinear regression (NLR) model, and posts the results on a website. The automated models were similar to previous NLR models, first applied to forecasting ozone in the Louisville metro area. The forecast system operated reliably during the 2004 and 2005 O₃ seasons, producing at least one forecast per day better than 99% of the time. The forecast accuracy of the automated system was good. For all 2004 and 2005 forecasts, the mean absolute error was equal to 8.7 ppb, or 15.6% of the overall mean concentration. The overall detection rate of air quality standard exceedences was 56%, and the overall false alarm rate was 42%. In Louisville, the performance of the automated system was comparable to that of expert forecasters using the NLR model as a forecast tool.     

A comparison of nonlinear regression and neural network models for ground-level ozone forecasting

A hybrid nonlinear regression (NLR) model and a neural network (NN) model, each designed to forecast next-day maximum 1-hr average ground-level O3 concentrations in Louisville, KY, were compared for two O3 seasons--1998 and 1999. The model predictions were compared for the forecast mode, using forecasted meteorological data as input, and for the hindcast mode, using observed meteorological data as input. The two models performed nearly the same in the forecast mode. For the two seasons combined, the mean absolute forecast error was 12.5 ppb for the NLR model and 12.3 ppb for the NN model. The detection rate of 120 ppb threshold exceedances was 42% for each model in the forecast mode. In the hindcast mode, the NLR model performed marginally better than the NN model. The mean absolute hindcast error was 11.1 ppb for the NLR model and 12.9 ppb for the NN model. The hindcast detection rate was 92% for the NLR model and 75% for the NN model.     

Evaluation of the Mesoscale Meteorological Model (MM5)-Community Multi-Scale Air Quality Model (CMAQ) performance in hindcast and forecast of ground-level ozone

This paper presents the first attempt to apply the Mesoscale Meteorological Model (MM5)-Community Multi-Scale Air Quality Model (CMAQ) model system to simulate ground-level ozone (O3) over the continental Southeast Asia (CSEA) region for both hindcast and forecast purposes. Hindcast simulation was done over the CSEA domain for two historical O3 episodes, January 26-29, 2004 (January episode, northeast monsoon) and March 24-26, 2004 (March episode, southwest monsoon). Experimental forecast was done for next-day hourly O3 during January 2006 over the central part of Thailand (CENTHAI). Available data from 20 ambient monitoring stations in Thailand and 3 stations in Ho Chi Minh City, Vietnam, were used for the episode analysis and for the model performance evaluation. The year 2000 anthropogenic emission inventory prepared by the Center for Global and Regional Environmental Research at the University of Iowa was projected to the simulation year on the basis of the regional average economic growth rate. Hourly emission in urban areas was prepared using ambient carbon monoxide concentration as a surrogate for the emission intensity. Biogenic emissions were estimated based on data from the Global Emissions Inventory Activity. Hindcast simulations (CSEA) were performed with 0.5 degree x 0.5 degree resolution, whereas forecast simulations (CENTHAI) were done with 0.1 degree x 0.1 degree hourly emission input data. MM5-CMAQ model system performance during the selected episodes satisfactorily met U.S. Environmental Protection Agency criteria for O3 for most simulated days. The experiment forecast for next-day hourly O3 in January 2006 yielded promising results. Modeled plumes of ozone in both hindcast and forecast cases agreed with the main wind fields and extended over considerable downwind distances from large urban areas.     

Elevated atmospheric CO2 concentration and temperature across an urban–rural transect

The heat island effect and the high use of fossil fuels in large city centers are well documented, but by how much fossil fuel consumption is elevating atmospheric CO₂ concentrations and whether elevations in both atmospheric CO₂ and air temperature from rural to urban areas are consistently different from year to year are less well known. Our aim was to record atmospheric CO₂ concentrations, air temperature and other environmental variables in an urban area and compare it to suburban and rural sites to see if urban sites are experiencing climates expected globally in the future with climate change. A transect was established from Baltimore city center (Urban site), to the outer suburbs of Baltimore (suburban site) and out to an organic farm (rural site). At each site a weather station was set-up to monitor environmental variables for 5 years. Atmospheric CO₂ was consistently and significantly increased on average by 66 ppm from the rural to the urban site over the 5 years of the study. Air temperature was also consistently and significantly higher at the urban site (14.8 °C) compared to the suburban (13.6 °C) and rural (12.7 °C) sites. Relative humidity was not different between sites whereas the vapor pressure deficit (VPD) was significantly higher at the urban site compared to the suburban and rural sites. An increase in nitrogen deposition at the rural site of 0.6% and 1.0% compared to the suburban and urban sites was small enough not to affect soil nitrogen content. Dense urban areas with large populations and high vehicular traffic have significantly different microclimates compared to outlying suburban and rural areas. The increases in atmospheric CO₂ and air temperature are similar to changes predicted in the short term with global climate change, therefore providing an environment suitable for studying future effects of climate change on terrestrial ecosystems.