Trends and Relationships of O3, NOx and HC in the South Coast Air Basin of California

A basin-wide air quality trend analysis for the South Coast Air Basin of California is conducted for hydrocarbons (HC), NO_x, O₃ and CO using multi-station composite daily maximum-hour average ambient concentrations for the third quarter (July, August and September) from 1968 to 1985. Emissions and air quality trends are compared for the period 1968-1984. Ambient HC and NOX trends are somewhat different from estimated emission trends of HC and NO_x, while a definite, downward trend of ambient CO is consistent with vehicular emission control measures. Basin-wide ambient HC, NO_x and O₃ appear to show downward trends for the period 1970-1985, but because of high fluctuations it is difficult to delineate trends for shorter periods. The meteorology (850 mb temperature)-adjusted O₃ shows a more consistent downward trend than does unadjusted O₃. Polynomial and multiplicative regression models for basin-wide empirical O₃-HC-NO_x relationships Indicate that the O₃ variation is explained largely by the meteorological variable (850 mb temperature) although model estimations are improved by adding HC and NO_x concentration terms.     

A new measurement method for nitrogen oxides in the air using an annular diffusion scrubber coated with titanium dioxide

A new convenient measurement method of nitrogen oxides (NO_x) in the ambient air was developed. The collection of NO_x is performed by an annular diffusion scrubber coated with a mixture of titanium dioxide (TiO₂) and hydroxyapatite (Ca₁₀(PO₄)₆(OH)₂) and the analysis is carried out by ion chromatography with conductivity detection. Under ultraviolet light (UV) illumination, TiO₂ produces reactive oxygen species such as super oxide (O₂⁻), hydroxyl radical (OH·) and peroxyhydroxyl radical (HO₂·), by which nitric oxide (NO) is oxidized to nitrogen dioxide (NO₂), and is further oxidized to nitric acid (HNO₃). The yielded HNO₃ and NO₂ are effectively adsorbed on the surface of TiO₂ and hydroxyapatite. The collection efficiencies of NO and NO₂ by the annular diffusion scrubber coated with the catalysts under UV illumination are higher than 98%, respectively, at the air flow rate of 0.2–1.0 l min⁻¹. After the collection of NO_x, by feeding deionized water into the annular diffusion scrubber, HNO₃ and NO₂ which adsorbed on the catalysts are extracted as forms of nitrite ion (NO₂⁻) and nitrate ion (NO₃⁻). The extraction efficiencies of NO and NO₂ are almost 100%. The activity of the washed catalysts can be completely recovered by drying with the purified air. Further, a simultaneous separated measurement of NO and NO₂ can be performed by utilizing the UV illumination dependence. This method was applied to the measurement of NO_x in the ambient air. The NO_x concentration measured by this method was in good agreement with that obtained using the chemiluminescence NO_x analyzer.     

Variation of surface ozone in Campo Grande,Brazil: meteorological effect analysis and prediction

The effect of meteorological variables on surface ozone (O₃) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O₃ concentration, relative humidity and solar radiation. The threshold model that considers two O₃ regimes was the one that correctly describes the effect of important meteorological variables in O₃ behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O₃ regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O₃ concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O₃ behaviour, being useful to define policy strategies for human health protection regarding air pollution.     

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.     

An assessment of the impact of climate change on air quality at two UK sites

Possible effects of climate change on air quality are studied for two urban sites in the UK, London and Glasgow. Hourly meteorological data were obtained from climate simulations for two periods representing the current climate and a plausible late 21st century climate. Of the meteorological quantities relevant to air quality, significant changes were found in temperature, specific humidity, wind speed, wind direction, cloud cover, solar radiation, surface sensible heat flux and precipitation. Using these data, dispersion estimates were made for a variety of single sources and some significant changes in environmental impact were found in the future climate. In addition, estimates for future background concentrations of NO_x, NO₂, ozone and PM₁₀ upwind of London and Glasgow were made using the meteorological data in a statistical model. These showed falls in NO_x and increases in ozone for London, while a fall in NO₂ was the largest percentage change for Glasgow. Other changes were small. With these background estimates, annual-average concentrations of NO_x, NO₂, ozone and PM₁₀ were estimated within the two urban areas. For London, results averaged over a number of sites showed a fall in NO_x and a rise in ozone, but only small changes in NO₂ and PM₁₀. For Glasgow, the changes in all four chemical species were small. Large-scale background ozone values from a global chemical transport model are also presented. These show a decrease in background ozone due to climate change. To assess the net impact of both large scale and local processes will require models which treat all relevant scales.     

Laboratory and field measurements of NOx produced from corona discharge

This research consists of a laboratory study and a field study. The laboratory research reports the formation of NO_x from a point to plane corona discharge. Discharge polarity and relative humidity determined the amount of NO_x that was produced. The positive point discharge caused more NO_x to form than the negative point discharge. For both polarities NO_x production showed a nonlinear increase with current. Relative humidity enhanced the NO_x formation for both polarities. In each case, the amount of NO_x formed was comparable to the quantity of N₂O produced from corona discharge. The research also reports the results from a field study that measured the amounts of 03 and NO₂ produced by corona discharge during a thunderstorm. The study found that the ambient concentrations of 03 and NO₂ increased several fold due to corona discharge and returned to original levels after the thunderstorm.     

石家庄市道路交通环境NO_x污染水平及ARMA模型预测

选择石家庄市区代表性路段作为研究对象,对其交通环境空气中NOx的污染水平进行现状监测。基于Matlab软件建立拟合模型,对下一时期的NOx污染趋势进行预测。结果表明,石家庄市交通环境中NOx小时浓度介于0.047~0.237 mg/m3之间,呈早晚高,且下午明显低于上午的日变化规律;NOx日均浓度介于0.076~0.211 mg/m3之间,其浓度与车流量呈明显的正相关性。利用matlab软件建立的ARMA模型能够较好地预测道路交通环境空气中NOx的浓度变化趋势。     

Conversion of nitrogen oxides on commercial photocatalytic dispersion paints

In the present study, photocatalytic reactions of nitrogen oxides (NO_x = NO + NO₂) were studied on commercial TiO₂ doped facade paints in a flow tube photoreactor under simulated atmospheric conditions. Fast photocatalytic conversion of NO and NO₂ was observed only for the photocatalytic paints and not for non-catalytic reference paints. Nitrous acid (HONO) was formed in the dark on all paints studied, however, it efficiently decomposes under irradiation only on the photocatalytic samples. Thus, it is concluded that photocatalytic paint surfaces do not represent a daytime source of HONO, in contrast to other recent studies on pure TiO₂ surfaces. As main final product, the formation of adsorbed nitric acid/nitrate anion (HNO₃/NO₃^?) was observed with near to unity yield. In addition, traces of H₂O₂ were observed in the gas phase only in the presence of O₂. Formation of the greenhouse gas nitrous oxide (N₂O) could be excluded. The uptake kinetics of NO, NO₂ and HONO was very fast under atmospheric conditions (e.g. γ(NO + TiO₂) > 10^?5). Thus, the uptake on urban surfaces (painted houses, etc.) will be limited by transport. For a hypothetically painted street canyon, an average reduction of nitrogen oxide levels of ca. 5% is estimated. Since the harmful HNO₃/NO₃^? is formed on the surface of the photoactive paints, whereas it is formed in the gas phase in the atmosphere, the use of photocatalytic paints may also help to reduce acid deposition, e.g. on plants, or nitric acid related health issues.     

Ground level ozone concentrations and its association with NOx and meteorological parameters in Kathmandu valley, Nepal

This paper summarizes the results of a yearlong continuous measurements of gaseous pollutants, NO, NO₂, NO_x and O₃ in the ambient air at Kathmandu valley. Measured concentration of the pollutants in study area is a function of time. NO, NO₂ and O₃ peak occurred in succession in presence of sunlight. At the time of maximum O₃ concentration most of the NO_x are utilized. The diurnal cycle of ground level ozone concentrations, revealed mid-day peak with lower nocturnal concentrations and inverse relationship exists between O₃ and NO_x, which are evidences of photochemical O₃ formation. The observed ground level ozone during monsoon is slight lower than the pre-monsoon value. Further, lack of rainfall and higher temperature, solar radiation in the pre-monsoon have given rise to the gradual build up of ozone and it is lowest during winter. Ground level ozone concentrations measured during bandha (general strike) and weekend are 19% and 13% higher than those measured during weekdays. The most effective ozone abatement strategy for Kathmandu Valley may be control of NO_x emissions.     

A Review of Techniques Available for Estimating Short-Term NO2 concentrations

The Environmental Protection Agency is reviewing the need for a short-term NO₂ standard based on an averaging time of three hours or less. State Implementation plans and New Source Reviews will require air quality simulation techniques capable of estimating ambient NO₂ concentrations. There is a need for multi-source (urban) models and for point source models.

A review of currently available techniques for the estimation of NO₂ concentrations resulting from NO_x point sources is presented. The available methods include simple screening techniques and refined reactive plume models. The screening techniques first use a standard gaussian dispersion model to estimate the maximum 1 hr NO_x concentration caused by the source. The second step involves estimating the fraction of this NO* concentration occurring as NO₂.

Reactive plume models numerically simulate the simultaneous effects of dispersion and chemistry on NO₂ concentrations. Organic as well as inorganic reactions are incorporated. Reactive plume models should be used, where screening techniques indicate the potential for violation of the NO₂ standard.

Current generation reactive plume models neglect the effect of turbulent concentration fluctuation on NO₂ formation and use inappropriately large dispersion coefficients to estimate plume concentrations. Approaches being developed to resolve these problems are discussed.     

NMOC,ozone, and organic aerosol in the southeastern United States, 1999–2007: 2. Ozone trends and sensitivity to NMOC emissions in Atlanta,Georgia

Non-methane organic carbon (NMOC) measurements made in Atlanta, Georgia from 1999–2007 are used with nitrogen oxide (NO_x or NO_y) and ozone (O₃) data to investigate relationships between O₃ precursors and peak 8-hour O₃ concentrations in the city. Data from a WNW-to-ENE transect of sites illustrate that the mean urban peak 8-hour O₃ excess constitutes about 20% of the peak 8-hour O₃ measured at the area-wide maximum O₃ site when air-mass movement is from the northwest quadrant; local influence is potentially greater on days with more stagnation or recirculation. The peak 8-hour O₃ concentrations in Atlanta increase as (1) surface temperature (T), ambient NMOC and NO_y concentrations, and previous-day peak O₃ concentrations increase, and as (2) relative humidity, surface wind speeds, and ratios of NMOC-to-NO_y decrease. An observation-based statistical model is introduced to relate area-wide peak 8-hour O₃ concentrations to ambient NMOC and NO_y concentrations, while accounting for the non-linear dependences of peak 8-hour O₃ concentrations on meteorological factors. On the majority of days when the area-wide peak 8-hour O₃ exceeds 75 ppbv, meteorologically-adjusted peak 8-hour O₃ concentrations increase as ambient NMOC concentrations increase (NMOC sensitive) and ambient NO_y concentrations decrease. This result contrasts with regional conditions in which O₃ formation appears to be NO_x-sensitive in character. The results offer observationally-based information of relevance to O₃ management strategies in the Atlanta area, potentially contributing to “weight-of-evidence” assessments.     

Solar driven nitrous acid formation on building material surfaces containing titanium dioxide: A concern for air quality in urban areas?

The photoenhanced uptake of nitrogen dioxide (NO₂) to the surface of commercially available self-cleaning window glass has been studied under controlled laboratory conditions. This material is one of an array of modern building products which incorporate titanium dioxide (TiO₂) nanoparticles and are finding increasing use in populated urban areas. Amongst the principal drivers for the use of these materials is that they are thought to facilitate the irreversible removal of pollutants such as NO₂ and organic molecules from the atmosphere and thus act to remediate air quality. While it appears that TiO₂ materials do indeed remove organic molecules from built environments, in this study we show that the photoenhanced uptake of NO₂ to one example material, self-cleaning window glass, is in fact accompanied by the substantial formation (50–70%) of gaseous nitrous acid (HONO). This finding has direct and serious implications for the use of these materials in urban areas. Not only is HONO a harmful respiratory irritant, it is also readily photolysed by solar radiation leading to the formation of hydroxyl radicals (OH) together with the re-release of NO_x as NO. The net effect of subsequent OH initiated chemistry can then be the further degradation of air quality through the formation of secondary pollutants such as ozone and VOC oxidation products. In summary, we suggest that a scientifically conceived technical strategy for air quality remediation based on this technology, while widely perceived as universally beneficial, could in fact have effects precisely opposite to those intended.     

Effects of Instrument Precision and Spatial Variability on the Assessment of the Temporal Variation of Ambient Air Pollution in Atlanta,Georgia

Abstract

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO₂, CO, NO_x, and O₃) and fine particulate matter ([PM_2.5] PM_2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7–40% of the temporal variation in the daily pollutant measures and was largest for the PM_2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO₂, CO, NO_x, and EC). Population–weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O₃ to 70% of the temporal variation for SO₂ and EC. Wind rose plots, corrected for diurnal and seasonal pattern effects, are used to demonstrate the impacts of local sources on monitoring station data. The results presented are being used to quantify the impacts of instrument precision and spatial variability on the assessment of health effects of ambient air pollution in Atlanta and are relevant to the interpretation of results from time series health studies that use data from fixed monitors.     

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.     

Radical precursors and related species from traffic as observed and modeled at an urban highway junction

Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals and are believed to favor ozone formation significantly. Traffic emission data for both compounds are scarce and mostly outdated. A better knowledge of today's HCHO and HONO emissions related to traffic is needed to refine air quality models. Here the authors report results from continuous ambient air measurements taken at a highway junction in Houston, Texas, from July 15 to October 15, 2009. The observational data were compared with emission estimates from currently available mobile emission models (MOBILE6; MOVES [MOtor Vehicle Emission Simulator]). Observations indicated a molar carbon monoxide (CO) versus nitrogen oxides (NO_x) ratio of 6.01 ± 0.15 (r ² = 0.91), which is in agreement with other field studies. Both MOBILE6 and MOVES overestimate this emission ratio by 92% and 24%, respectively. For HCHO/CO, an overall slope of 3.14 ± 0.14 g HCHO/kg CO was observed. Whereas MOBILE6 largely underestimates this ratio by 77%, MOVES calculates somewhat higher HCHO/CO ratios (1.87) than MOBILE6, but is still significantly lower than the observed ratio. MOVES shows high HCHO/CO ratios during the early morning hours due to heavy-duty diesel off-network emissions. The differences of the modeled CO/NO_x and HCHO/CO ratios are largely due to higher NO_x and HCHO emissions in MOVES (30% and 57%, respectively, increased from MOBILE6 for 2009), as CO emissions were about the same in both models. The observed HONO/NO_x emission ratio is around 0.017 ± 0.0009 kg HONO/kg NO_x which is twice as high as in MOVES. The observed NO₂/NO_x emission ratio is around 0.16 ± 0.01 kg NO₂/kg NO_x, which is a bit more than 50% higher than in MOVES. MOVES overestimates the CO/CO₂ emission ratio by a factor of 3 compared with the observations, which is 0.0033 ± 0.0002 kg CO/kg CO₂. This as well as CO/NO_x overestimation is coming from light-duty gasoline vehicles.

Implications: Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals that ultimately contribute to ozone formation. There still exist uncertainties in emission sources of HONO and HCHO and thus regional air quality modeling still tend to underestimate concentrations of free radicals in the atmosphere. This paper demonstrates that the latest U.S. Environmental Protection Agency (EPA) traffic emission model MOVES still shows significant deviations from observed emission ratios, in particular underestimation of HCHO/CO and HONO/NO_x ratios. Improving the performance of MOVES may improve regional air quality modeling.     

Estimating Future NO2 Levels in the Greater Los Angeles Area by Source–Type Contribution

Two indicator pollutants, carbon monoxide (CO) for mobile source influence and sulfur dioxide (SO₂) for stationary source influence, were used to estimate source-type contributions to ambient NO₂ levels in a base year and to predict NO₂ concentrations in a future year. For a specific source-receptor pair, the so-called influence coefficient of each of three source categories (mobile sources, power plants, and other stationary sources) was determined empirically from concurrent measurements of CO and SO₂ concentrations at the receptor site and CO and SO₂ emissions from each source category in the source area. Those coefficients, which are considered time invariant, were used in conjunction with the base year and future year NO _x emission values to estimate source-type contribution to ambient NO₂ levels at seven study sites selected from the Greater Los Angeles area for both the base year period, 1974 through 1976, and the future goal year of 1987 in which the air quality standards for NO₂ are to be attained. The estimated NO₂ air quality at the seven sites is found to meet the national annual standard of 5 pphm and over 99.9% of total hours, the California 1-hr NO₂ standard of 25 pphm in 1987. The estimated power plant contributions to ambient NO₂ levels are found to be considerably smaller than those to total NO _x emissions in the area. Providing that reasonably complete air quality and emissions data are available, the present analysis method may prove to be a useful tool in evaluating source contributions to both short-term peak and long-term average NO₂ concentrations for use in control strategy development.     

Coupling of titania with multiwall carbon nanotubes for decomposition of gas-phase pollutants under simulated indoor conditions

This study synthesized multiwall carbon nanotube (MWNT)–titania (TiO₂) composites and examined their characteristics and photocatalytic performance for the cleaning of gas-phase benzene, toluene, ethyl benzene, and o-xylene (BTEX) under simulated indoor conditions. Optical and spectral surveys of the as-synthesized composite confirmed that the TiO₂ nanoparticles were bound intimately to the MWNT networks. The photocatalytic performance was evaluated using an annular-type reactor inner-coated with MWNT–TiO₂ or Degussa P25 TiO₂. The composite revealed gas removal ability superior to that of stand-alone TiO₂. This composite was also less affected by humidity during toluene decomposition compared to the previous result obtained from a stand-alone TiO₂. Unlike another previous result obtained from the TiO₂, the performance of the composite was not affected by changes in input concentration (IC) within a simulated indoor air quality range (0.1–1.0 ppm) but it decreased significantly when the IC was increased to 5 and 10 ppm. As the flow rate was decreased from 4.0 to 1.0 L min^?1, the average efficiency for the target compounds increased to 95% or ～100%. The MWNT–TiO₂ composite could be applied effectively to the decomposition for BTEX under certain simulated indoor conditions.

Implications: Unlike water applications, there are few reports of gas-phase applications of multiwall carbon nanotubes (MWCNT)–TiO₂ composites. This study found that MWCNT–TiO₂ composites showed performance in the removal of toxic gaseous aromatic superior to that of stand-alone TiO₂. In addition, the pollutant degradation efficiency of the composite was less affected by humidity than for a stand-alone TiO₂ unit within a simulated indoor relative humidity range. Moreover, unlike the TiO₂ unit, the composite's performance was not affected by variations in the input concentrations within the simulated indoor air quality (IAQ) range. In addition, the decomposition efficiencies increased to 100% with decreasing flow rate.     

Application of Artificial Neural Networks to Modeling and Prediction of Ambient Ozone Concentrations

ABSTRACT

The deterministic modeling of ambient O₃ concentrations is difficult because of the complexity of the atmospheric system in terms of the number of chemical species; the availability of accurate, time-resolved emissions data; and the required rate constants. However, other complex systems have been successfully approximated using artificial neural networks (ANNs). In this paper, ANNs are used to model and predict ambient O₃ concentrations based on a limited number of measured hydrocarbon species, NO_x compounds, temperature, and radiant energy. In order to examine the utility of these approaches, data from the Coastal Oxidant Assessment for Southeast Texas (COAST) program in Houston, TX, have been used. In this study, 53 hydrocarbon compounds, along with O₃, nitrogen oxides, and meteorological data were continuously measured during summer 1993. Steady-state ANN models were developed to examine the ability of these models to predict current O₃ concentrations from measured VOC and NO concentrations. To predict the future concentrations of O₃, dynamic models were also explored and were used for extraction of chemical information such as reactivity estimations for the VOC species.

The steady-state model produced an approximation of O₃ data and demonstrated the functional relationship between O₃ and VOC-NO_x concentrations. The dynamic models were able to the adequately predict the O₃ concentration and behavior of VOC-NO_x-O₃ system a number of hourly intervals into the future. For 3 hr into the future, O₃ concentration could be predicted with a root-mean squared error (RMSE) of 8.21 ppb. Extending the models further in time led to an RMSE of 11.46 ppb for 5-hr-ahead values. This prediction capability could be useful in determining when control actions are needed to maintain measured concentrations within acceptable value ranges.     

An investigation of the assumptions of linear chemistry and superposition in LRTAP models

Most current LRTAP models assume a linear relationship between sulphur emissions and predicted acidic concentrations, as well as the ability to superpose concentrations from different emission sources. This paper uses a non-linear chemistry model and control scenarios of 50% reductions in S, N and hydrocarbon (HC) emissions to examine the validity of these two assumptions at various downwind receptors. The model predicts that a 50% reduction in S emissions will lead to a 60–65% reduction in SO₂ concentrations and a 25–40% reduction in H₂SO₄ concentrations, depending upon whether or not NO_x and/or HC emissions are reduced by 50% at the same time. The non-linearities in the model predictions are due to complex interactions between NO_x, HC, OH and HO₂. Even when there was non-linearity in the individual S species (SO₂ and SO₄), there was little non-linearity in total airborne S. Adding the results of independent model predictions for different sources (superposition) might introduce errors due to

• 1.(1) chemical interactions between the emissions from the various sources
• 2.(2) overestimates of physical processes such as mixing in of ambient air.

The model was also used to examine trends in regional air quality in eastern North Amierica.     

Further Validation of Artificial Neural Network-Based Emissions Simulation Models for Conventional and Hybrid Electric Vehicles

Abstract

With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO₂] and oxides of nitrogen [NO_x]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO₂ and NO_x in the case of a class-8 truck but were more accurate as the truck weight increased.