Temporal variations in atmospheric CO2 concentrations in Kuwait City,Kuwait with comparisons to Phoenix,Arizona, USA

Hourly atmospheric carbon dioxide (CO2) concentration measurements are available from 1996 to present for a suburban site within the growing metropolitan area of Kuwait City. Analyses of this record reveal (a) an annual cycle with highest values in February and lowest values in September reflecting the growth and decay of vegetation in the Northern Hemisphere as well as fluctuations in motor traffic, (b) a weekly cycle with highest values during the weekdays and lowest values during weekends, and (c) a diurnal cycle with highest values after sunset when the local atmosphere becomes more stable following vehicular emission of CO2 throughout the day and lowest values in late afternoon following several hours of relatively unstable conditions. During the daytime, CO2 concentrations are related to wind direction, with westerly winds (coming from the desert) promoting lowest CO2 concentrations. At night, lowest CO2 levels are associated with higher wind speeds and winds from the north. The findings from the Kuwait City area, particularly when contrasted with the situation in Phoenix, further our understanding of the dynamics of CO2 levels in urban environments.     

北京市交通干道空气中CO2和δ13C变化及来源分析

采用离轴积分腔输出光谱技术分析了夏季和冬季北京市四环路空气中CO2浓度和δ13C值变化特征。结果表明,四环路上车流量大,夏季和冬季均超过15万辆/d,06:00-10:00和15:00-19:00车行缓慢,平均车速小于25 km/h,车道占有率超过60%。机动车排放出大量的CO2,导致四环路的空气中CO2浓度呈双峰曲线日变化和δ13C值呈双波谷曲线日变化。同位素定量区分结果显示,四环路空气中CO2主要来源于机动车尾气排放,夏季和冬季均在52%以上。逐步回归分析表明,平均车速和车道占有率是影响四环路上CO2浓度和δ13C值的主要因子。06:00-10:00和15:00-19:00来源于汽车尾气的CO2比例比整个白天来源于汽车尾气的CO2比例高出6个百分点以上,表明车行缓慢显著增加了CO2的排放。     

城市节假日前后碳通量特征及其与车流量关系

全球变化背景下,城市作为主要的碳源,对其碳循环的研究成为陆地生态系统碳循环的重点内容之一。以上海市奉贤区为研究对象,基于涡度相关技术,结合定点连续观测的车流量数据,分析节假日（元旦）前后CO2浓度和碳通量的变化特征,及其与车流量的关系。结果表明,CO2浓度和通量日变化呈现明显的双峰型曲线,节假日CO2浓度（385.6mg/L）平均值低于工作日（401.1 mg/L）。在本研究时段内该系统表现为碳源,尽管在白天的某些时段是碳汇,表明城市系统碳通量受自然和人为2个因素共同作用,自然因素比如该系统中的香樟、雪松等常绿植物的光合作用,人为因素由人类活动造成。基于车流量与交通流量的线性回归分析表明,机动车量碳排放对于碳通量变化产生18%的贡献。     

Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM_2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM_2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM_2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM_2.5 concentrations, a likely result of underestimating PM_2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.     

Indirect Source Impact Analysis-Carbon Monoxide Modeling

This paper reviews current methods and models used in estimating the impacts of indirect sources on CO air quality, an important process in rapidly growing areas. The paper gives an overview of the modeling process, reviews how to obtain fleet average emission factors, presents a commonly used set of worst-case meteorology, identifies dispersion models available for predicting local CO concentrations and tells how to predict an 8-hour average CO concentration given a 1-hour prediction. The paper also discusses background CO concentrations and some of the issues involved in choosing reasonable receptor locations. Several problems exist with indirect source impact analysis—in both the technical area and the policy area. Increased effort is needed to correct these problems, especially to quantify the probability of the worst-case meteorology and to define the locations of reasonable receptors.     

Distributions and concentrations of PAHs in Hong Kong soils

Surface soil (0-10 cm) samples from 53 sampling sites including rural and urban areas of Hong Kong were collected and analyzed for 16 EPA priority polycyclic aromatic hydrocarbons (PAHs). Total PAH concentrations were in the range of 7.0-410 microg kg(-1) (dry wt), with higher concentrations in urban soils than that in rural soils. The three predominant PAHs were Fluoranthene, Naphthalene and Pyrene in rural soils, while Fluoranthene, Naphthalene and Benzo(b + k)fluoranthene dominated the PAHs of urban soils. The values of PAHs isomer indicated that biomass burning might be the major origin of PAHs in rural soils, but vehicular emission around the heavy traffic roads might contribute to the soil PAHs in urban areas. A cluster analysis was performed and grouped the detectable PAHs under 4 clusters, which could be indicative of the PAHs with different origins and PAHs affected by soil organic carbon contents respectively.     

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.     

Long-term investigation of atmospheric mercury contamination in Connecticut.

Atmospheric mercury was monitored from January 1997 through the end of December 1999 in eight sampling locations in Connecticut. Four sampling locations were chosen along the shores of Long Island Sound and four were chosen in interior sections of Connecticut. Sampling locations were chosen to represent both rural and urban sectors. Average concentrations of gaseous and particulate mercury were found to be 2.06 ng/m3 and 10.5 pg/m3, respectively. The weekly average wet deposition fluxes of mercury and methylmercury over the three-year sampling period were measured to be 611 and 11 microg/ha/week, respectively. Concentrations of gaseous, particulate and wet flux of mercury were found to be significantly higher in urban areas than the rural sampling locations. There was, however, no significant difference between the mean gaseous and particulate concentrations of mercury in coastal and inland sampling locations. No significant difference was observed either between the wet fluxes of total mercury in coastal and inland sampling locations and there was no spatial gradient for mercury concentration and deposition. The data of this study suggest that vehicular traffic and localized emission sources in urban areas play a significant role in determining the atmospheric concentration of mercury in Connecticut.     

Develop dynamic model for predicting traffic CO emissions in urban areas

The greater the use of energy in the transportation sectors, the higher the emission of carbon monoxide (CO), and hence inevitable harm to environment and human health. In this concern, measuring and predicting of CO emission from transportation sector—especially large cities—is important as it constitute 90 % of all CO emission. Many urban cities in developing world have not properly experienced such measurements or predictions. In this paper, for the first time, field measurements of traffic characteristics data and corresponding CO concentration have been performed for developing a model for predicting CO emissions from transportation sector for New Borg El Arab (NBC), Egypt. The performance of Swiss-German Handbook Emission Factors for Road Transport (HBEFA v3.1) model has been assessed for predicting the CO concentration at roadside in the study area. Results indicated that HBEFA v3.1 underestimate emission figures. The developed CO dynamic emission model involves the traffic flow characteristics with roadside CO concentrations. Acceptable representation of measured CO concentration has been shown by the developed dynamic CO emission model which introduces R ²?=?0.77, mean biases and frictional biases of ?0.27 mg m^?3 and 0.09, respectively. A comparison between predicted CO concentrations using HBEFA v3.1 and the promoted dynamic model indicate that HBEFA v3.1 estimates CO emission concentrations in the study area with a mean error and frictional biases 159.26 and 233.33 %, respectively, higher than those of the developed model.     

Characterization of atmospheric particulates, particle-bound transition metals and polycyclic aromatic hydrocarbons of urban air in the centre of Athens (Greece)

The concentrations of trace metals and polycyclic aromatic hydrocarbons (PAHs) adsorbed to total suspended particulate (TSP) and finer fractions of airborne particulate matter (PM) were determined from a site in the centre of Athens (Greece), which is characterized by heavy local traffic and is densely populated, during the winter and summer periods in 2003-2004. Also, we collected and analyzed samples of diesel and gasoline exhaust particles from local vehicles (buses, taxis and private cars) and from chimney exhaust of residential central heating appliances. A seasonal effect was observed for the size distribution of aerosol mass, with a shift to larger fine fractions in winter. The most commonly detected trace metals in the TSP and PM fractions were Fe, Pb, Zn, Cu, Cr, V, Ni and Cd and their concentrations were similar to levels observed in heavily polluted urban areas from local traffic and other anthropogenic emissions. Analysis of 16 PAHs bound to PM showed that they are mostly traffic related. In general, the fine particulate PAHs concentrations were higher than coarse particles. The most common PAHs in PM(10.2) and PM(2.1) were pyrene, phenanthrene, acenapthylene and fluoranthene, which are associated with diesel and gasoline exhaust particles. The results of this study underlined the importance of local emission sources, especially vehicular traffic, central heating and other local anthropogenic emissions. Compared with other big cities, Athens has much higher levels of airborne particles, especially of the finer fractions PM(10) and PM(2.5), correlated with traffic-related air pollution.     

Hot emission model for mobile sources: application to the metropolitan region of the city of Santiago, Chile

Depending on the final application, several methodologies for traffic emission estimation have been developed. Emission estimation based on total miles traveled or other average factors is a sufficient approach only for extended areas such as national or worldwide areas. For road emission control and strategies design, microscale analysis based on real-world emission estimations is often required. This involves actual driving behavior and emission factors of the local vehicle fleet under study. This paper reports on a microscale model for hot road emissions and its application to the metropolitan region of the city of Santiago, Chile. The methodology considers the street-by-street hot emission estimation with its temporal and spatial distribution. The input data come from experimental emission factors based on local driving patterns and traffic surveys of traffic flows for different vehicle categories. The methodology developed is able to estimate hourly hot road CO, total unburned hydrocarbons (THCs), particulate matter (PM), and NO(x) emissions for predefined day types and vehicle categories.     

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.     

A mobile pollutant measurement laboratory—measuring gas phase and aerosol ambient concentrations with high spatial and temporal resolution

A mobile pollutant measurement laboratory was designed and built at the Paul Scherrer Institute (Switzerland) for the measurement of on-road ambient concentrations of a large set of trace gases and aerosol parameters with high time resolution (<15 s for most instruments), along with geographical and meteorological information. This approach allowed for pollutant level measurements both near traffic (e.g. in urban areas or on freeways/main roads) and at rural locations far away from traffic, within short periods of time and at different times of day and year. Such measurements were performed on a regular base during the project year of gas phase and aerosol measurements (YOGAM). This paper presents data measured in the Zürich (Switzerland) area on a late autumn day (6 November) in 2001. The local urban particle background easily reached 50 000 cm⁻³, with additional peak particle number concentrations of up to 400 000 cm⁻³. The regional background of the total particle number concentration was not found to significantly correlate with the distance to traffic and anthropogenic emissions of carbon monoxide and nitrogen oxides. On the other hand, this correlation was significant for the number concentration of particles in the size range 50–150 nm, indicating that the particle number concentration in this size range is a better traffic indicator than the total number concentration. Particle number size distribution measurements showed that daytime urban ambient air is dominated by high number concentrations of ultrafine particles (nanoparticles) with diameters <50 nm, which are immediately formed by traffic exhaust and thus belong to the primary emissions. However, significant variation of the nanoparticle mode was also observed in number size distributions measured in rural areas both at daytime and nighttime, suggesting that nanoparticles are not exclusively formed by primary traffic emissions. While urban daytime total number concentrations were increased by a factor of 10 compared to the nighttime background, corresponding factors for total surface area and total volume concentrations were 2 and 1.5, respectively.     

Investigation of respirable suspended particulate trend and relevant environmental factors in Hong Kong downtown areas

As the addressing of high demand of good air quality in urban area, a study on air pollutant dispersion and distribution resulting from vehicular exhaust emission is strongly required. In particular, vehicular exhaust emission has become a major air pollution source in metropolitan city like Hong Kong, which is characterized with the heavy, dense traffic flow and the limited land resources. Respirable suspended particulate (RSP) is one of main pollutants resulted from vehicular exhaust emission in urban area. Hence, in this study, we focus on analyzing the variation of RSP levels including diurnal, monthly and annual patterns at selected roadsides in Hong Kong during the period of 1998--2005. Furthermore, the relationships between RSP level and the relevant meteorological factors such as temperature, rainfall and wind conditions in Hong Kong territory have been discussed as well.     

Prediction of PM10 concentrations at urban traffic intersections using semi-empirical box modelling with instantaneous velocity and acceleration

At urban traffic intersections, vehicles frequently stop with idling engines during the red-light period and speed up rapidly during the green-light period. The changes of driving patterns (i.e., idle, acceleration, deceleration and cruising patterns) generally produce uncertain emission. Additionally, the movement of pedestrians and the influence of wind further result in the random dispersion of pollutants. It is, therefore, too complex to simulate the effects of such dynamics on the resulting emission using conventional deterministic causal models.For this reason, a modified semi-empirical box model for predicting the PM₁₀ concentrations on roadsides is proposed in this paper. The model constitutes three parts, i.e., traffic, emission and dispersion components. The traffic component is developed using a generalized force traffic model to obtain the instantaneous velocity and acceleration when vehicles move through intersections. Hence the distribution of vehicle emission in street canyon during the green-light period is calculated. Then the dispersion component is investigated using a semi-empirical box model combining average wind speed, box height and background concentrations. With these considerations, the proposed model is applied and evaluated using measured data at a busy traffic intersection in Mong Kok, Hong Kong. In order to test the performance of the model, two situations, i.e., the data sets within a sunny day and between two sunny days, were selected to examine the model performance. The predicted values are generally well coincident with the observed data during different time slots except several values are overestimated or underestimated. Moreover, two types of vehicles, i.e., buses and petrol cars, are separately taken into account in the study. Buses are verified to contribute most to the emission in street canyons, which may be useful in evaluating the impact of vehicle emissions on the ambient air quality when there is a significant change in a specific vehicular population.     

Use of Subsampled Traffic Data to Estimate Roadway Emissions,Including Conversion to MOBILE6 Vehicle Classifications

Abstract

Air quality is degraded by many factors, among which the emissions from on‐road vehicles play a significant role. Timely and accurate estimate of such emissions becomes very important for policy‐making and effective control measures. However, lack of traffic data and outdated emission software make this task difficult. This research has demonstrated a new method that facilitates the vehicular emission inventories at the local level by using shorter-time Highway Performance Monitoring System (HPMS) traffic data along with the latest U.S. Environment Protection Agency (EPA) emission modeling software, MOBILE6. The conversion methodology was developed for converting readily available HPMS traffic volume data into EPA MOBILE-based traffic classifications, and a corresponding software program was written for automating the process. EPA MOBILE6 model was used to obtain emissions of nitrogen oxides (NO_x), volatile organic compound (VOC), and cabon monoxide (CO) emitted by the parent traffic and subsampled traffic data, and these emissions were additionally compared. The case study has shown that the difference of the magnitude between the emission estimates produced by certain subsampled and parent traffic data are minor, indicating that subsampled HPMS data can be used for reporting parent traffic emissions. It was also observed that traffic emissions follow a Weibull distribution, and NO_x emissions were more sensitive to the traffic data composition than VOC and CO. Lastly, use of average emission values of 20 or 30 consecutive minutes appears to be valid for representing hourly emissions.     

Carbon monoxide modeling from transportation sources.

Street canyon module and gaussian line source module of a regional-scale dispersion model Indic Airviro were used to simulate ambient carbon monoxide (CO) concentrations due to traffic flow at two roadside monitoring locations in Singapore. The fleet average emission factors for each vehicle category was estimated from US EPA MOBILE 5 A guidelines as a function of speed, vehicle deterioration rates and model years. 1-h CO concentrations and worst case 8-h levels have been simulated and compared with measured readings. This study used model-simulated rooftop concentration levels from non-localized sources as background levels at the two sites. The resulting CO concentrations correlate well with actual measured levels and provide a unique approach to predict the impact of CO from transportation.     

Use of subsampled traffic data to estimate roadway emissions, including conversion to MOBILE6 vehicle classifications

Air quality is degraded by many factors, among which the emissions from on-road vehicles play a significant role. Timely and accurate estimate of such emissions becomes very important for policy-making and effective control measures. However, lack of traffic data and outdated emission software make this task difficult. This research has demonstrated a new method that facilitates the vehicular emission inventories at the local level by using shorter-time Highway Performance Monitoring System (HPMS) traffic data along with the latest U.S. Environment Protection Agency (EPA) emission modeling software, MOBILE6. The conversion methodology was developed for converting readily available HPMS traffic volume data into EPA MOBILE-based traffic classifications, and a corresponding software program was written for automating the process. EPA MOBILE6 model was used to obtain emissions of nitrogen oxides (NOx), volatile organic compound (VOC), and cabon monoxide (CO) emitted by the parent traffic and subsampled traffic data, and these emissions were additionally compared. The case study has shown that the difference of the magnitude between the emission estimates produced by certain subsampled and parent traffic data are minor, indicating that subsampled HPMS data can be used for reporting parent traffic emissions. It was also observed that traffic emissions follow a Weibull distribution, and NOx emissions were more sensitive to the traffic data composition than VOC and CO. Lastly, use of average emission values of 20 or 30 consecutive minutes appears to be valid for representing hourly emissions.     

A composite receptor and dispersion model approach for estimation of effective emission factors for vehicles

It is difficult to estimate vehicular emission factors at traffic junctions for use in dispersion modelling studies. Firstly, because the vehicles are in various modes of operation and secondly, it is difficult to delineate the effects of other contributing sources, mainly the effects of road dust and deposited constituents, which are very prominent at traffic junctions in India. Factor analysis-multiple regression (FA-MR), a receptor modelling technique has been used in this study for apportioning the contributing sources. The measurement data consist of one year's temporal variation of suspended particulate matter (SPM), analysed for its trace metal constituents, and two gaseous components NO₂ and SO₂ at two traffic junctions in Mumbai (India). FA-MR apportioned 40% of the observed SPM to road dust and 15% to vehicular sources. Of the total Pb observed in the SPM, FA-MR apportioned 60% to vehicular sources and 20% to road dust. The field-observed vehicular counts, meteorological parameters and road geometry were used in California line source dispersion model to estimate the effective vehicular emission factor for Pb at one traffic junction. This derived emission factor was used to predict the Pb concentration at second (independent observation) traffic junction. The result was found to be more satisfactory than using default emission factors obtained from literature. Similarly, effective vehicular emission factor for NO₂ was also evaluated for one site and tested for predicting concentrations at the other site.     

Changes in air quality at near-roadway schools after a major freeway expansion in Las Vegas,Nevada

Near-roadway ambient black carbon (BC) and carbon monoxide (CO) concentrations were measured at two schools adjacent to a freeway and at an urban background school 2 km from the freeway to determine the change in concentrations attributable to vehicle emissions after the three-lane expansion of U.S. Highway 95 (US 95) in Las Vegas, Nevada. Between summer 2007 and summer 2008, average weekday small-vehicle volume increased by 40% ± 2% (standard error). Average weekday large-vehicle volume decreased by 17% ± 5%, due to a downturn in the economy and an associated decline in goods movement. Average vehicle speed increased from 58 to 69 mph, a 16% ± 1% increase. The authors compared BC and CO concentrations in summer 2007 with those in summer 2008 to understand what effect the expansion of the freeway may have had on ambient concentrations: BC and CO were measured 17 m north of the freeway sound wall, CO was measured 20 m south of the sound wall, and BC was measured at an urban background site 2 km south of the freeway. Between summer 2007 and summer 2008, median BC decreased at the near-road site by 40% ± 2% and also decreased at the urban background site by 24% ± 4%, suggesting that much of the change was due to decreases in emissions throughout Las Vegas, rather than only on US 95. CO concentrations decreased by 14% ± 2% and 10% ± 3% at the two near-road sites. The decrease in BC concentrations after the expansion is likely due to the decrease in medium- and heavy-duty-vehicle traffic resulting from the economic recession. The decrease in CO concentrations may be a result of improved traffic flow, despite the increase in light-duty-vehicle traffic.

ImplicationsMonitoring of BC and CO at near-road locations in Las Vegas demonstrated the impacts of changes in traffic volume and vehicle speed on near-road concentrations. However, urban-scale declines in concentrations were larger than near-road changes due to the impacts of the economic recession that occurred contemporaneously with the freeway expansion.