Evaluation of a Gaussian and a Lagrangian model against a roadside data set,with emphasis on low wind speed conditions

The evaluation of the high percentiles of concentration distributions is required by most national air quality guidelines, as well as the EU directives. However, it is problematic to compute such high percentiles in stable, low wind speed or calm conditions. This study utilizes the results of a previous measurement campaign near a major road at Elimäki in southern Finland in 1995, a campaign specifically designed for model evaluation purposes. In this study, numerical simulations were performed with a Gaussian finite line source dispersion model CAR-FMI and a Lagrangian dispersion model GRAL, and model predictions were compared with the field measurements. In comparison with corresponding results presented previously in the literature, the agreement of measured and predicted data sets was good for both models considered, as measured using various statistical parameters. For instance, considering all NO_x data (N=587), the so-called index of agreement values varied from 0.76 to 0.87 and from 0.81 to 1.00 for the CAR-FMI and GRAL models, respectively. The CAR-FMI model tends to slightly overestimate the NO_x concentrations (fractional bias FB=+14%), while the GRAL model has a tendency to underestimate NO_x concentrations (FB=−16%). The GRAL model provides special treatment to account for enhanced horizontal dispersion in low wind speed conditions; while such adjustments have not been included in the CAR-FMI model. This type of Lagrangian model therefore predicts lower concentrations, in conditions of low wind speeds and stable stratification, in comparison with a standard Lagrangian model. In low wind speed conditions the meandering of the flow can be quite significant, leading to enhanced horizontal dispersion. We also analyzed the difference between the model predictions and measured data in terms of the wind speed and direction. The performance of the CAR-FMI model deteriorated as the wind direction approached a direction parallel to the road, and for the lowest wind speeds. However, the performance of the GRAL model varied less with wind speed and direction; the model simulated better the cases of low wind speed and those with the wind nearly parallel to the road.     

A semi-empirical model for urban PM10 concentrations,and its evaluation against data from an urban measurement network

We have developed a model for evaluating the mass-based concentrations of urban particulate matter. The basic model assumption is that local vehicular traffic is responsible for a substantial fraction of the street-level concentrations of both PM₁₀ and NO_x, either due to primary emissions or resuspension from street surfaces. The modelling system utilises the data from an air quality monitoring network in the Helsinki Metropolitan Area. We have determined linear relationships between the measured urban PM₁₀ data against those of NO_x in various urban surroundings, based on continuously measured hourly concentration values. The data was obtained from two stations in central Helsinki and one suburban station in the Helsinki Metropolitan Area during a period of 3 yr, from 1996 to 1998. The model also includes a treatment of the regional background concentrations, and resuspended particulate matter. The model performance was evaluated against the measured PM₁₀ data from the above-mentioned three stations and from two other stations, using data that was measured in 1999. We used two alternative model versions, one based on separate correlation parameters (PM₁₀ vs. NO_x) for each station, and another based on parameters averaged over the stations considered. We analysed the agreement between the measured and predicted hourly concentration time series, utilising the values of the fractional bias (FB) and the so-called index of agreement (IA). As expected, the model predicts relatively well the yearly mean concentrations of PM₁₀: the FB values range from −0.05 to +0.09. Model performance is also relatively good when predicting the yearly mean values that are classified separately for each hour of the day: the corresponding IA values range from 0.85 to 0.96. However, model performance is substantially worse in predicting the hourly time series of the year: the IA values using the station-specific parameters range from 0.46 to 0.65. The model was applied in evaluating the yearly average spatial concentration distribution of PM₁₀ in central Helsinki, based on the corresponding modelled NO_x concentrations. With re-evaluation of a few parameters that can be determined empirically, the model could be evaluated, and most probably applied, in other urban areas as well.     

A measurement campaign in a street canyon in Helsinki and comparison of results with predictions of the OSPM model

In 1997, a measuring campaign was conducted in a street canyon (Runeberg St.) in Helsinki. Hourly mean concentrations of CO, NO_x, NO₂ and O₃ were measured at street and roof levels, the latter in order to determine the urban background concentrations. The relevant hourly meteorological parameters were measured at roof level; these included wind speed and direction, temperature and solar radiation. Hourly street level measurements and on-site electronic traffic counts were conducted throughout the whole of 1997; roof level measurements were conducted for approximately two months, from 3 March to 30 April in 1997. CO and NO_x emissions from traffic were computed using measured hourly traffic volumes and evaluated emission factors. The Operational Street Pollution Model (OSPM) was used to calculate the street concentrations and the results were compared with the measurements. The overall agreement between measured and predicted concentrations was good for CO and NO_x (fractional bias were −4.2 and +4.5%, respectively), but the model overpredicted the measured NO₂ concentrations (fractional bias was +22%). The agreement between the measured and predicted values was also analysed in terms of its dependence on wind speed and direction; the latter analysis was performed separately for two categories of wind velocity. The model qualitatively reproduces the observed behaviour very well. The database, which contains all measured and predicted data, is available for further testing of other street canyon dispersion models. The dataset contains a larger proportion of low wind speed cases, compared with other available street canyon measurement datasets.     

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.     

Analysis of air quality within a street canyon using statistical and dispersion modelling techniques

The dispersion model, ADMS-Urban, alongside the statistical modelling technique, generalized additive modelling, have been used to predict hourly NO_x and nitrogen dioxide (NO₂) concentrations at a busy street canyon location and the results compared with measurements. Generalized additive models (GAMs) were constructed for NO₂ and NO_x concentrations using input data required to run ADMS-Urban. Bivariate polar plots have been produced from the wind flow (speed and direction) and pollution data (measured and predicted concentrations) to provide further information regarding the complex wind-pollutant interactions in an urban street canyon. The predictions made with the GAMs show excellent agreement with measured concentrations at this location, reproducing both the magnitude of NO_x and NO₂ concentrations and also the wind speed-wind direction dependence of pollutant sources within the canyon. However, the predictions made with ADMS-Urban under-estimated the measured NO_x by 11% and NO₂ by 21% and there are clear differences in the bivariate polar plots. Several sensitivity tests were carried out with ADMS-Urban in an attempt to produce predictions in closer agreement to those measured at Gillygate. Increasing the primary NO₂ fraction in ADMS-Urban (from 10% to 20%) had a considerable effect on the predictions made with this model, increasing NO₂ predictions by ∼20%. However, the bivariate plots still showed major differences to those of the measurements. This work illustrates that generalized additive modelling is a useful tool for investigating complex wind-pollutant interactions within a street canyon.     

An inexpensive method to estimate CO and NOx emissions from mobile sources

CO and NO_x measurements from mobile sources at two urban locations in Córdoba City, Argentina, were used to develop a very simple method to estimate emission from these sources. This development was possible because primary urban air pollution in Córdoba comes mostly from mobile sources and because a field measurement campaign was conducted by the city government during 1995–1996 that has allowed us to have a complete and valuable data bank. Air concentrations of CO, NO_x as well as physical, and meteorological variables were measured at two urban sites with two monitoring stations. We compared the measured CO and NO_x air concentration data with the predictions of a method that uses regression analysis to estimate the emission factor from the mobile sources. The agreement is good, considering the simplicity of the approach.     

Summertime photochemical ozone formation in Santiago,Chile

The city of Santiago, Chile experiences frequent high pollution episodes and as a consequence very high ozone concentrations, which are associated with health problems including increasing daily mortality and hospital admissions for respiratory illnesses. The development of ozone abatement strategies requires the determination of the potential of each pollutant to produce ozone, taking into account known mechanisms and chemical kinetics in addition to ambient atmospheric conditions. In this study, the photochemical formation of ozone during a summer campaign carried out from March 8–20, 2005 has been investigated using an urban photochemical box model based on the Master Chemical Mechanism (MCMv3.1). The MCM box model has been constrained with 10 min averages of simultaneous measurements of HONO, HCHO, CO, NO, j(O¹D), j(NO₂), 31 volatile organic compounds (VOCs) and meteorological parameters. The O₃–NO_x–VOC sensitivities have been determined by simulating ozone formation at different VOC and NO_x concentrations. Ozone sensitivity analyses showed that photochemical ozone formation is VOC-limited under average summertime conditions in Santiago. The results of the model simulations have been compared with a set of potential empirical indicator relationships including H₂O₂/HNO₃, HCHO/NO_y and O₃/NO_z. The ozone forming potential of each measured VOC has been determined using the MCM box model. The impacts of the above study on possible summertime ozone control strategies in Santiago are discussed.     

The relation between ozone,NOx and hydrocarbons in urban and polluted rural environments

Research over the past ten years has created a more detailed and coherent view of the relation between O₃ and its major anthropogenic precursors, volatile organic compounds (VOC) and oxides of nitrogen (NO_x). This article presents a review of insights derived from photochemical models and field measurements. The ozone–precursor relationship can be understood in terms of a fundamental split into a NO_x-senstive and VOC-sensitive (or NO_x-saturated) chemical regimes. These regimes are associated with the chemistry of odd hydrogen radicals and appear in different forms in studies of urbanized regions, power plant plumes and the remote troposphere. Factors that affect the split into NO_x-sensitive and VOC-sensitive chemistry include: VOC/NO_x ratios, VOC reactivity, biogenic hydrocarbons, photochemical aging, and rates of meteorological dispersion. Analyses of ozone–NO_x–VOC sensitivity from 3D photochemical models show a consistent pattern, but predictions for the impact of reduced NO_x and VOC in indivdual locations are often very uncertain. This uncertainty can be identified by comparing predictions from different model scenarios that reflect uncertainties in meteorology, anthropogenic and biogenic emissions. Several observation-based approaches have been proposed that seek to evaluate ozone–NO_x–VOC sensitivity directly from ambient measurements (including ambient VOC, reactive nitrogen, and peroxides). Observation-based approaches have also been used to evaluate emission rates, ozone production efficiency, and removal rates of chemically active species. Use of these methods in combination with models can significantly reduce the uncertainty associated with model predictions.     

Application of CALINE4 to roadside NO/NO2 transformations

The CALINE4 roadway dispersion model has been applied to concentrations of NO_x and NO₂ measured near Gandy Boulevard in Tampa, FL (USA) during May 2002. A NO_x emission factor of 0.86 gr mi⁻¹ was estimated by treating NO+NO₂ (NO_x) as a conserved species and minimizing the differences between measured and calculated NO_x concentrations. This emission factor was then used to calculate NO₂ concentrations using the NO/NO₂ transformation reactions built into CALINE4. A comparison of measured and calculated NO₂ concentrations indicates that for ambient O₃ concentrations less than 40 ppb the model under-predicts the chemical transformation of NO. The enhanced transformation of NO may be due to reactions of NO with oxidants such as peroxy radicals that are present either in the atmosphere or in vehicle exhaust.     

Statistical and diagnostic evaluation of a new-generation urban dispersion modelling system against an extensive dataset in the Helsinki area

We have developed a modelling system for predicting the traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. This paper describes a comparison of the NO_x and NO₂ concentrations predicted using this modelling system with the results of an urban air quality monitoring network. We performed a statistical analysis to determine the agreement between predicted and measured hourly time series of concentrations at four permanently located and three mobile monitoring stations in the Helsinki Metropolitan Area in 1996–1997 (at a total of ten urban and suburban measurement locations). At the stations considered, the so-called index of agreement values of the predicted and measured time series of the NO₂ concentrations vary between 0.65 and 0.82, while the fractional bias values range from −0.29 to +0.26. In comparison with corresponding results presented in the literature, the agreement between the measured and predicted datasets is good, as indicated by these statistical parameters. The seasonal variations of the NO₂ concentrations were analysed in terms of the relevant meteorological parameters. We also analysed the difference between model predictions and measured data diagnostically, in terms of meteorological parameters, including wind speed and direction (the latter separately for two wind speed classes), atmospheric stability and ambient temperature, at two monitoring stations in central Helsinki. The modelling system tends to overpredict the measured NO₂ concentrations both at the highest (u⩾6 m s⁻¹) and at the lowest wind speeds (u<2 m s⁻¹). For higher wind speeds, the modelling system overpredicts the measured NO₂ concentrations in certain wind direction intervals; specific ranges were found for both monitoring stations considered. The modelling system tends to underpredict the measured concentrations in convective atmospheric conditions, and overpredict in stable conditions. The possible physico-chemical reasons for these differences are discussed.     

Measurement of surface ozone and its precursors in an urban area in South Brazil

Weekly and seasonal variations of surface ozone and their precursors – nitrogen oxides, carbon monoxide-associated with meteorological parameters (wind direction, temperature, solar radiation) – are reported. Measurements were performed continuously during 2006 at two sampling stations located in the metropolitan area of Porto Alegre, Brazil. Results have shown that O₃ concentrations remained almost constant between weekdays. Levels of NO_x precursors decreased especially on Sundays, due to lighter traffic. The seasonal variation has shown a maximum O₃ concentration during summer and spring while NO_x and NO₂ have maxima at the colder months. The daily cycle of highest ozone concentrations reveals a lower nightly level and an inverse relation between O₃ and NO_x, evidencing the photochemical formation of O₃. There are seasonal variation and source heterogeneity.     

Aircraft measurements of O3, NOx,CO, VOCs,and SO2 in the Yangtze River Delta region

In this study, air pollutants, including ozone (O₃), nitrogen oxides (NO_x = NO + NO₂), carbon monoxides (CO), sulfur dioxide (SO₂), and volatile organic compounds (VOCs) measured in the Yangtze River Delta (YRD) region during several air flights between September/30 and October/11 are analyzed. This measurement provides horizontal and vertical distributions of air pollutants in the YRD region. The analysis of the result shows that the measured O₃ concentrations range from 20 to 60 ppbv. These values are generally below the US national standard (84 ppbv), suggesting that at the present, the O₃ pollutions are modest in this region. The NO_x concentrations have strong spatial and temporal variations, ranging from 3 to 40 ppbv. The SO₂ concentrations also have large spatial and temporal variations, ranging from 1 to 35 ppbv. The high concentrations of CO are measured with small variations, ranging from 3 to 7 ppmv. The concentrations of VOCs are relatively low, with the total VOC concentrations of less than 6 ppbv. The relative small VOC concentrations and the relative large NO_x concentrations suggest that the O₃ chemical formation is under a strong VOC-limited regime in the YRD region. The measured O₃ and NO_x concentrations are strongly anti-correlated, indicating that enhancement in NO_x concentrations leads to decrease in O₃ concentrations. Moreover, the O₃ concentrations are more sensitive to NO_x concentrations in the rural region than in the city region. The ratios of Δ[O₃]/Δ[NO_x] are ?2.3 and ?0.25 in the rural and in the city region, respectively. In addition, the measured NO_x and SO₂ concentrations are strongly correlated, highlighting that the NO_x and SO₂ are probably originated from same emission sources. Because SO₂ emissions are significantly originated from coal burnings, the strong correlation between SO₂ and NO_x concentrations suggests that the NO_x emission sources are mostly from coal burned sources. As a result, the future automobile increases could lead to rapid enhancements in O₃ concentrations in the YRD region.     

The effects of congestions tax on air quality and health

The “Stockholm Trial” involved a road pricing system to improve the air quality and reduce traffic congestion. The test period of the trial was January 3–July 31, 2006. Vehicles travelling into and out of the charge cordon were charged for every passage during weekdays. The amount due varied during the day and was highest during rush hours (20 SEK = 2.2 EUR, maximum 60 SEK per day). Based on measured and modelled changes in road traffic it was estimated that this system resulted in a 15% reduction in total road use within the charged cordon. Total traffic emissions in this area of NO_x and PM10 fell by 8.5% and 13%, respectively. Air quality dispersion modelling was applied to assess the effect of the emission reductions on ambient concentrations and population exposure. For the situations with and without the trial, meteorological conditions and other emissions than from road traffic were kept the same. The calculations show that, with a permanent congestion tax system like the Stockholm Trial, the annual average NO_x concentrations would be lower by up to 12% along the most densely trafficked streets. PM10 concentrations would be up to 7% lower. The limit values for both PM10 and NO₂ would still be exceeded along the most densely trafficked streets. The total population exposure of NO_x in Greater Stockholm (35 × 35 km with 1.44 million people) is estimated to decrease with a rather modest 0.23 μg m^?3. However, based on a long-term epidemiological study, that found an increased mortality risk of 8% per 10 μg m^?3 NO_x, it is estimated that 27 premature deaths would be avoided every year. According to life-table analysis this would correspond to 206 years of life gained over 10 years per 100 000 people following the trial if the effects on exposures would persist. The effect on mortality is attributed to road traffic emissions (likely vehicle exhaust particles); NO_x is merely regarded as an indicator of traffic exposure. This is only the tip of the ice-berg since reductions are expected in both respiratory and cardiovascular morbidity. This study demonstrates the importance of not only assessing the effects on air quality limit values, but also to make quantitative estimates of health impacts, in order to justify actions to reduce air pollution.     

Behaviour and variability of local and regional oxidant levels (OX = O3 + NO2) measured in a polluted area in central-southern of Iberian Peninsula

The purpose of this work is to contribute to the understanding of the photochemical air pollution in central-southern of the Iberian Peninsula, analysing the behaviour and variability of oxidant levels (OX?=?O₃?+?NO₂), measured in a polluted area with the highest concentration of heavy industry in central Spain. A detailed air pollution database was observed from two monitoring stations. The data period used was 2008 and 2009, around 210,000 data, selected for its pollution and meteorological statistics, which are very representative of the region. Data were collected every 15 min, however hourly values were used to analyse the seasonal and daily ozone, NO, NO₂ and OX cycles. The variation of OX concentrations with NO _x is investigated, for the first time, in the centre of the Iberian Peninsula. The concentration of OX was calculated using the sum of a NO _x -independent ‘regional’ contribution (i.e. the O₃ background), and a linearly NO _x -dependent ‘local’ contribution. Monthly dependence of regional and local OX concentration was observed to determine when the maximum values may be expected. The variation of OX concentrations with levels of NO _x was also measured, in order to pinpoint the atmospheric sources of OX in the polluted areas. The ratios [NO₂]/[OX] and [NO₂]/[NO _x ] vs. [NO _x ] were analysed to find the fraction of OX in the form of NO₂, and the possible source of the local NO _x -dependent contribution, respectively. The progressive increase of the ratio [NO₂]/[OX] with [NO _x ] observed shows a greater proportion of OX in the form of NO₂ as the level of NO _x increases. The higher measured values in the ratio [NO₂]/[NO _x ] should not be attributed to NO _x emissions by vehicles; they could be explained by industrial emission, termolecular reactions or formaldehyde and HONO directly emitted by vehicles exhausts. We also estimate the rate of NO₂ photolysis, J _NO2?=?0.18–0.64 min^?1, a key atmospheric reaction that influence O₃ production and then the regional air quality. The first surface plot study of annual variation of the daily mean oxidant levels, obtained for this polluted area may be used to improve the atmospheric photochemical dynamic in this region of the Iberian Peninsula where there are undeniable air quality problems.     

Regression modelling of hourly NOx and NO2 concentrations in urban air in London

Based on hourly measurements of NO_x NO₂ and O₃ and meteorological data, an ordinary least squares (OLS) model and a first-order autocorrelation (AR) model were developed to analyse the regression and prediction of NO_x and NO₂ concentrations in London. Primary emissions and wind speed are the most important factors influencing NO_x concentrations; in addition to these two, reaction of NO with O₃ is also a major factor influencing NO₂ concentrations. The AR model resulted in high correlation coefficients (R > 0.95) for the NO_x and NO₂ regression based on a whole year's data, and is capable of predicting NO₂ (R = 0.83) and NO_x (R = 0.65) concentrations when the explanatory variables were available. The analysis of the structure of regression models by Principal Component Analysis (PCA) indicates that the regression models are stable. The results of the OLS model indicate that there was an exceptional NO₂ source, other than primary emission and reaction of NO with O₃, in the air pollution episode in London in December 1991.     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Sensitivity of ozone concentrations to diurnal variations of surface emissions in Mexico City: A WRF/Chem modeling study

Sensitivity of ozone (O₃) concentrations in the Mexico City area to diurnal variations of surface air pollutant emissions is investigated using the WRF/Chem model. Our analysis shows that diurnal variations of nitrogen oxides (NO_x = NO + NO₂) and volatile organic compound (VOC) emissions play an important role in controlling the O₃ concentrations in the Mexico City area. The contributions of NO_x and VOC emissions to daytime O₃ concentrations are very sensitive to the morning emissions of NO_x and VOCs. Increase in morning NO_x emissions leads to decrease in daytime O₃ concentrations as well as the afternoon O₃ maximum, while increase in morning VOC emissions tends to increase in O₃ concentrations in late morning and early afternoon, indicating that O₃ production in Mexico City is under VOC-limited regime. It is also found that the nighttime O₃ is independent of VOCs, but is sensitive to NO_x. The emissions of VOCs during other periods (early morning, evening, and night) have only small impacts on O₃ concentrations, while the emissions of NO_x have important impacts on O₃ concentrations in the evening and the early morning.This study suggests that shifting emission pattern, while keeping the total emissions unchanged, has important impacts on air quality. For example, delaying the morning emission peak from 8 am to 10 am significantly reduced the morning peaks of NO_x and VOCs, as well as the afternoon O₃ maxima. It suggests that without reduction of total emission, the daytime O₃ concentrations can be significantly reduced by changing the diurnal variations of the emissions of O₃ precursors.     

Modelling and assessing trends in traffic-related emissions using a generalised additive modelling approach

A generalised additive modelling (GAM) approach is used to model daily concentrations of nitrogen oxides (NO_X), nitrogen dioxide (NO₂), carbon monoxide (CO), benzene and 1,3-butadiene at a busy street canyon location in central London. The models were developed for the period July 1998–June 2005 using appropriate meteorological and road traffic covariates. For all models, the complex and localised wind-flow patterns resulting from the street canyon location of the monitoring site, which can be difficult to model deterministically, have a large influence on the model predictions. It is shown that GAMs built using simple covariates explain a large amount of the daily variation for these pollutants (mean r²=0.86). It is found that concentrations of benzene and 1,3-butadiene have declined in line with detailed calculations of emissions trends, with some evidence to suggest that reductions in benzene have been greater than estimated reductions in emissions. Although measured concentrations of NO_X have declined from 1998 to 2005, much of the decline appears to be associated with reductions in overall traffic and meteorological factors rather than reduced emissions of NO_X. Unadjusted NO_X trends show a 28.6% reduction (95% confidence interval from 21.2% to 35.8%) from 1998 to 2005, whereas meteorologically adjusted trends show a 19.3% decline (95% confidence interval from 14.8% to 23.5%) over this period. Analysis shows that there were a higher number of occasions in the early part of the time series that led to strong recirculation of exhaust emissions and higher NO_X concentrations at this location, thus affecting observed trends in concentration.     

Simulation of long-range transport of acidic pollutants in East Asia during the Yellow Sand event

The atmospheric chemical process was simulated using the Carbon Bond 4 (CB-4) model, the aqueous-phase chemistry in Regional Acid Deposition Model and the thermodynamic equilibrium relation of aerosols with the emission inventories of the Emission Database for Global Atmospheric Research, the database of China and South Korea and the Mesoscale Model version 2 (MM5) meteorological fields to examine the spatial distributions of the acidic pollutant concentrations in East Asia for the case of the long-lasting Yellow Sand event in April 1998. The present models simulate quite well the observed general trend and the diurnal variation of concentrations of gaseous pollutants, especially for O₃ concentration. However, the model underestimates SO₂ and NO_x concentration but overestimates O₃ concentration largely due to uncertainty in NO_x and VOC emissions. It is found that the simulated gaseous pollutants such as SO₂, NO_x, and NH₃ are not transported far away from the source regions but show significant diurnal variations of their concentrations. However, the daily variations of the concentrations are not significant due to invariant emission rates. On the other hand, concentrations of the transformed pollutants including SO₄²⁻, NH₄⁺, and NO₃⁻ are found to have significant daily variations but little diurnal variations. The model-estimated deposition indicates that dry deposition is largely contributed by gaseous pollutants while wet deposition of pollutants is mainly contributed by the transformed pollutants.     

Characterizations of chemical oxidants in Mexico City: A regional chemical dynamical model (WRF-Chem) study

The formation of chemical oxidants, particularly ozone, in Mexico City were studied using a newly developed regional chemical/dynamical model (WRF-Chem). The magnitude and timing of simulated diurnal cycles of ozone (O₃), carbon monoxide (CO) and nitrogen oxides (NO_x)_, and the maximum and minimum O₃ concentrations are generally consistent with surface measurements. Our analysis shows that the strong diurnal cycle in O₃ is mainly attributable to photochemical variations, while diurnal cycles of CO and NO_x mainly result from variations of emissions and boundary layer height. In a sensitivity study, oxidation reactions of aromatic hydrocarbons (HCs) and alkenes yield highest peak O₃ production rates (20 and 18 ppbv h⁻¹, respectively). Alkene oxidations, which are generally faster, dominate in early morning. By late morning, alkene concentrations drop, and oxidations of aromatics dominate, with lesser contributions from alkanes and CO. The sensitivity of O₃ concentrations to NO_x and HC emissions was assessed. Our results show that daytime O₃ production is HC-limited in the Mexico City metropolitan area, so that increases in HC emissions increase O₃ chemical production, while increases in NO_x emissions decrease O₃ concentrations. However, increases in both NO_x and HC emissions yield even greater O₃ increases than increases in HCs alone. Uncertainties in HC emissions estimates give large uncertainties in calculated daytime O₃, while NO_x emissions uncertainties are less influential. However, NO_x emissions are important in controlling O₃ at night.