A semi-empirical box modeling approach for predicting the carbon monoxide concentrations at an urban traffic intersection

Emissions generated roadside and at intersections are observed to be affected when there is a sudden change in the traffic flow pattern or increase in the vehicular population, particularly, during peak hours and during special events. The vehicles that queue up at traffic intersections spend a longer amount of time in idle driving mode generating more pollutant emissions per unit time. Other driving patterns (i.e., acceleration, deceleration and cruising) are also observed at intersections, affecting the emission pattern and therefore the resulting pollutant concentrations. The emission rate is not only affected by the increase in the vehicular population but also by the constantly changing traffic flow patterns and vehicles’ driving modes. The nature of the vehicle flows also affects the rate and nature of the dispersion of pollutants in the vicinity of the road, influencing the pollutant concentration. It is, therefore, too complex to simulate the effect of such dynamics on the resulting emission rates using conventional deterministic causal models.In view of this, a simple semi-empirical box model based on the ‘traffic flow rate’, is demonstrated in the present study for estimating the hourly average carbon monoxide (CO) concentrations on a 1-week data at one of the busiest traffic intersections in Delhi. The index of agreement for a whole week, was found to be 0.84, suggesting that the semi-empirical model is 84% error free. A value of 0.87 was found for weekdays and 0.75 for weekend days. The correlation coefficient for the whole week was found to be 0.75, with 0.78 for the weekdays and 0.62 for the weekend days. The RMSE and RRMSE were found to be 1.87% and 41% for a whole week, with 1.81% and 39.93% for the weekdays and 2.0% and 43.47% for the weekend days, respectively. Specific vehicle emission rates are optimized in this study for individual vehicle category, which may be useful in assessing their impacts on the air quality when there is a significant change in a specific vehicular population and the traffic pattern.     

The impact of natural and anthropogenic hydrocarbons on the tropospheric budget of carbon monoxide

A method to quantify the relative contributions of surface sources and photochemical production of atmospheric carbon monoxide has been implemented in a three-dimensional chemical-transport model. The impact of biogenic and anthropogenic hydrocarbons has been calculated. The oxidation of isoprene contributes to about 10% of the global tropospheric burden of carbon monoxide, with a maximum contribution over southern America and Africa. Oxidation of methane and terpenes contribute to 28 and 2%, respectively, of the tropospheric burden of CO. The oxidation of the other hydrocarbons, which include ethane, propane, ethylene, propylene and the surrogate hydrocarbon representing other hydrocarbons results in 12% of the CO tropospheric burden, among which 69% results from the oxidation of hydrocarbons of biologic origin. The overall global CO yield from the oxidation of isoprene is estimated to be 23% on a carbon basis. Comparisons between model results and the few available observations of isoprene, terpenes and their oxidation products show that there is no evidence that the current global isoprene emissions proposed in the IGAC/GEIA emissions data base are substantially overestimated, as suggested by previous studies.     

Climatology and forecast modeling of ambient carbon monoxide in Phoenix, Arizona

We perform a climatology of factors influencing ambient carbon monoxide (CO), in which we examine the relationships between meteorology, traffic patterns, and CO at seasonal, weekly, and diurnal time scales in Phoenix, Arizona. From this analysis we identify a range of potentially important variables for statistical CO modeling. Using stepwise multivariate regression, we create a suite of models for hourly and 8-h ambient CO designed for daily operational forecasting purposes. The resulting models include variables and interaction terms related to anticipated nocturnal atmospheric stability as well as antecedent and climatological CO behavior. The models are evaluated using a range of error statistics and skill measures. The most successful approach employs a two-stage modeling strategy in which an initial prediction is made that may, depending on the forecast value, be followed by a second prediction that improves upon the first. The best models provide accurate daily forecasts of CO, with explained variances approaching 0.9 and errors under 1 ppm.     

A statistical model for predicting carbon monoxide levels

This paper presents a statistical model that is able to predict carbon monoxide (CO) concentrations as a function of meteorological conditions and various air quality parameters. The experimental work was conducted in an urban atmosphere, where the emissions from cars are prevalent. A mobile air pollution monitoring laboratory was used to collect data, which were divided into two groups: a development group and a testing group. Only the development dataset was used for developing the model. The model was determined by using a stepwise multiple regression modelling procedure. Thirteen independent variables were selected as inputs: non-methane hydrocarbon (NMHC), methane (CH₄), suspended dust, carbon dioxide (CO₂), nitrogen oxide (NO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), wind speed, wind direction, temperature, relative humidity and solar energy. It was found that NO has the most effect on the predicted CO concentration. The contribution of NO to the CO concentration variations was 91.3%. Adding in the terms for NO₂), NMHC and CH₄ improved the model by only a further 2.3%. The derived model was shown to be statistically significant, and model predictions and experimental observations were shown to be consistent.     

Relating meteorological variables and trends in motor vehicle emissions to monthly urban carbon monoxide concentrations

A model which quantifies the relationship between the monthly time series for CO emissions, the monthly time series in ambient CO concentration, and meteorologically driven dispersion was developed. Fifteen cities representing a wide range of geographical and climatic conditions were selected. An eight-year time series (1984–1991 inclusive) of monthly averaged data were examined in each city. A new method of handling missing ambient concentration values which is designed to calculate city-wide average concentrations that follow the trend seen at individual monitor sites is presented. This method is general and can be used in other applications involving missing data. The model uses emissions estimates along with two meteorological variables (wind speed and mixing height) to estimate monthly averages of ambient air pollution concentrations. The model is shown to have a wide range of applicability; it works equally well for a wide range of cities that have very different temporal CO distributions. The model is suited for assessing long-term trends in ambient air pollutants and can also be used for estimating seasonal variations in concentration, estimation of trends in emissions, and for filling in gaps in the ambient concentration record.     

The role of a changing Arctic Ocean and climate for the biogeochemical cycling of dimethyl sulphide and carbon monoxide

Dimethyl sulphide (DMS) and carbon monoxide (CO) are climate-relevant trace gases that play key roles in the radiative budget of the Arctic atmosphere. Under global warming, Arctic sea ice retreats at an unprecedented rate, altering light penetration and biological communities, and potentially affect DMS and CO cycling in the Arctic Ocean. This could have socio-economic implications in and beyond the Arctic region. However, little is known about CO production pathways and emissions in this region and the future development of DMS and CO cycling. Here we summarize the current understanding and assess potential future changes of DMS and CO cycling in relation to changes in sea ice coverage, light penetration, bacterial and microalgal communities, pH and physical properties. We suggest that production of DMS and CO might increase with ice melting, increasing light availability and shifting phytoplankton community. Among others, policy measures should facilitate large-scale process studies, coordinated long term observations and modelling efforts to improve our current understanding of the cycling and emissions of DMS and CO in the Arctic Ocean and of global consequences.     

Spatial inhomogeneity in pollutant concentrations, and their implications for air quality model evaluation

The Southern California Air Quality Study database provides a valuable resource with which to test urban-scale photochemical models and to achieve a better understanding of the atmospheric dynamics of pollutant formation. The CIT model was evaluated using the SCAQS database according to traditional model performance guidelines. A first application, reported previously, focused on model enhancement and application of the model to the 27–29 August 1987 episode. This study evaluates the CIT model using the 24–25 June SCAQS episode, providing further evaluation of the model. Results show that the CIT airshed model can follow the diurnal variations of reactive species and the transport for relatively unreactive species. The normalized gross error for ozone was 31 % in June compared to 38% in August. However, to fully judge model performance in proper perspective, a question arises: “How well do the measurements reflect the air quality surrounding the monitoring station, not just in that location?” This is an important but seldom quantitatively considered factor, not only in model evaluation but in the study of health effects as well. Analyses indicate that individual concentration measurements only approximately represent the true volume-averaged concentrations within a computational grid cell and that significant spatial variations exist. Thus any evaluation of models using these data sets should take these local variations into consideration. A series of tests found that the local inhomogeneities had a normalized gross error in the range of 25–45% depending on the pollutant. In this context, the performance of the CIT model is consistent with known modeling limitations such as emissions inventories and sub-grid scale variation of observations.