Major Traffic Intersection Research Articles

Performance evaluation of portable dual-spot micro-aethalometers for source identification of black carbon aerosols: application to wildfire smoke and traffic emissions in the Pacific Northwest

Abstract. Black carbon (BC) is a component of particulate matter, emitted from the incomplete combustion of carbonaceous fuels. The presence of BC in the atmosphere can disrupt the atmospheric radiation budget, and exposure to BC can adversely affect human health. Multi-wavelength light-absorption-based dual-spot aethalometers can be used to quantify the source and characteristics of BC from traffic or biomass-burning-based sources. However, aethalometer measurements are affected by artifacts such as aerosol loading and light scattering; hence, they often need correction to reduce measurement uncertainty. This work assesses the performance of the recently developed portable aethalometer (MA300, AethLabs). Due to their portability and ease of usage, MA300s can be suitable for mobile and personal exposure monitoring. Here, we evaluate BC concentration and source apportionment accuracy of three MA300 units relative to a widely used aethalometer, the AE33 (Magee Scientific). Synchronous field measurements were performed at a major traffic intersection during regular and wildfire-smoke-affected days in Vancouver, Canada. We find that MA300-reported BC mass concentrations were strongly correlated (Slope range between 0.73 and 1.01, with R2 = 0.9) compared to the reference instrument, yet there is visible instrumental variability in the normalized concentrations (5 %) across three units. The mean absolute error of MA300-reported BC concentrations ranged between 0.44–0.98 µg m−3, with the highest deviations observed in wildfire-smoke-affected polluted days. From the aerosol light absorption measurement perspective, MA300s tend to underestimate the absorption coefficients (babs) across the five wavelengths. UV channel light absorption results were subjected to the highest amount of noise and were found to be consistently underestimating in all the MA300 units, leading to systematic bias in source apportionment analysis. Absorption Ångström exponent values from the MA300 units were able to capture the variability of aerosol sources within a day, with a mean value of 1.15 during clean days and 1.46 during wildfire-smoke-affected days. We investigated the application of the latest non-linear aethalometer correction protocols in the MA300 and found that flow fluctuations enhanced noise across all channels, compared to onboard instrument correction. We also identify that the UV (λ = 370 nm) channel absorption measurements are most sensitive to instrumental artifacts during the wildfire-smoke-affected period. Hence, as an alternative to traditional UV and IR (λ = 880 nm)-based BC source apportionment methods, in this work, we tested the blue (λ = 470 nm) and IR wavelengths for BC source apportionment calculation. When the blue–IR-based source apportionment technique is adopted instead of the UV–IR, there is a 10 % (on average) decrease in the percentage difference of the apportioned components from the reference monitor.

Application of biomagnetic analysis technique using roadside trees for monitoring and identification of possible sources of atmospheric particulates in selected air pollution hotspots in Delhi, India

With increasing atmospheric pollution and health issues associated with size of the particulate matter, it has become important to look for techniques that may improve the monitoring resolution. Magnetic bio-monitoring of particulate matter has been used in recent years in some countries as an approach for better spatial resolution that provides proxy indicators for the measurements over large areas. Delhi, which is one of the most polluted cities of not just India, but the whole world, is still probing to understand the possible sources. The present magnetic biomonitoring study was therefore, carried across different land use areas in some air pollution hotspots of Delhi, using common roadside tree species Morus alba, Ficus religiosa, Ficus virens and Ficus benghalensis to understand the magnitude and nature of the particulate pollution, and possible sources by studying magnetic properties (Magnetic susceptibility, Frequency-dependent susceptibility, S-ratio, and SIRM) of the dust deposited on leaves. Mass specific magnetic susceptibility (10−8 m3 kg−1) values were found to follow the order: Traffic intersection area (25.6–66.5) > Industrial area (25.4–41.3) > Residential area (13.2–30.1) > Institutional area serving as control (2.7–6.6). High magnetic susceptibility values indicated particulates with ferrimagnetic grains of anthropogenic or technogenic origin. Frequency-dependent Susceptibility indicated dominance of coarse multidomain (MD) and Pseudo Single Domain (PSD) +MD grains in industrial area and major traffic intersection. Average S ratio across all study sites ranged from 0.92 to 0.99 indicating presence of soft magnetic mineral with low coercivity. High SIRM values (10−5Am2 kg−1) from 58.1 to 862.3 suggested prevalence of magnetite dominating atmospheric particulates particularly in traffic intersection and industrial area, and to some extent in residential area. Morus alba and Ficus religiosa were found more suitable bio-monitors and the technique provided useful information on size, mineralogy and possible source of the particulates.

Ambient Air Quality Monitoring in Metropolitan City of Lagos, Nigeria

Twenty one sampling locations were assessed for carbon monoxide (CO), carbondioxide (CO 2 ), oxygen (O 2 ), sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), nitrogen oxide (NO), suspended particulate matter (SPM) and noise level using air pollutants measurement methods approved by ASTM for each specific parameter. All equipments and meters were all properly pre-calibrated before each usage for quality assurance. Findings of the study showed that measured levels of noise (61.4 - 101.4 dBA), NO (0.0 - 3.0 ppm), NO 2 (0.0 - 3.0 ppm), CO (1.0 – 42.0 ppm) and SPM (0.14 – 4.82 ppm) in all sampling areas were quite high and above regulatory limits however there was no significant difference except in SPM (at all the sampling points), and noise, NO 2 and NO (only in major traffic intersection). Air quality index (AQI) indicates that the ambient air can be described as poor for SPM, varied from good to very poor for CO, while NO and NO 2 are very good except at major traffic intersection where they were both poor and very poor (D-E). The results suggest that strict and appropriate vehicle emission management, industrial air pollution control coupled with close burning management of wastes should be considered in the study area to reduce the risks associated with these pollutants.© JASEM Keywords : Air, pollutants, Air quality index, carbon-monoxide, sulphur-dioxide

Statistical behavior of ozone in urban environment

Abstract This paper analyzes the statistical behavior of the ground level ozone concentrations (GLO) observed at a major traffic intersection in Delhi. Five sets of data, i.e. summer (May to July, high solar radiation data), winter (November to January, low solar radiation data), spring (March to April), autumn (September to October), and the entire year have been used to study the seasonal variation in the statistical behavior of GLO. Appropriate statistical distribution form has been identified from alternative candidate distribution models using the goodness-of-fit methods and parameters have been estimated using the method of maximum likelihood. The yearly, winters, spring, and summer datasets were found to follow the log-normal distribution model, while autumn dataset followed Weibull distribution. Analysis shows that ozone concentrations also show similar statistical behavior like other air pollutants and fit mainly to the log-normal distribution as reported for other pollutants in different studies. The seasonality of the datasets shows higher skewness during summers due to longish tail of the distribution mainly on account of higher photo–chemical activity. The probability density functions corresponding to the five datasets were used to compute the probability of exceedence of the National Ambient Air Quality Standards and return period of violation of standards. The distributions have also been used to classify the study region under various air quality descriptor categories. The region is found to violate the air quality compliance criteria 17% of the recorded times in the year. Alternative measures have been discussed to reduce the precursor emissions in order to achieve the air quality goals.

The Effect of Metro Expansions on Air Pollution in Delhi

The Delhi Metro (DM) is a mass rapid transit system serving the National Capital Region of India. It is also the world’s first rail project to earn carbon credits under the Clean Development Mechanism of the United Nations for reductions in CO2 emissions. Did the DM also lead to localized reduction in three transportation source pollutants? Looking at the period 2004–2006, one of the larger rail extensions of the DM led to a 34 percent reduction in localized CO at a major traffic intersection in the city. Results for NO2 are also suggestive of a decline, while those for PM25 are inconclusive due to missing data. These impacts of pollutant reductions are for the short run. A complete accounting of all long run costs and benefits should be done before building capital intensive metro rail projects.

The deployment of carbon monoxide wireless sensor network (CO-WSN) for ambient air monitoring.

Wireless sensor networks are becoming increasingly important as an alternative solution for environment monitoring because they can reduce cost and complexity. Also, they can improve reliability and data availability in places where traditional monitoring methods are difficult to site. In this study, a carbon monoxide wireless sensor network (CO-WSN) was developed to measure carbon monoxide concentrations at a major traffic intersection near the University of Cincinnati main campus. The system has been deployed over two weeks during Fall 2010, and Summer 2011–2012, traffic data was also recorded by using a manual traffic counter and a video camcorder to characterize vehicles at the intersection 24 h, particularly, during the morning and evening peak hour periods. According to the field test results, the 1 hr-average CO concentrations were found to range from 0.1–1.0 ppm which is lower than the National Ambient Air Quality Standards (NAAQS) 35 ppm on a one-hour averaging period. During rush hour periods, the traffic volume at the intersection varied from 2,067 to 3,076 vehicles per hour with 97% being passenger vehicles. Furthermore, the traffic volume based on a 1-h average showed good correlation (R2 = 0.87) with the 1-h average CO-WSN concentrations for morning and evening peak time periods whereas CO-WSN results provided a moderate correlation (R2 = 0.42) with 24 hours traffic volume due to fluctuated changes of meteorological conditions. It is concluded that the performance and the reliability of wireless ambient air monitoring networks can be used as an alternative method for real time air monitoring.

Hybrid modelling approach for effective simulation of reactive pollutants like Ozone

Prediction of air quality is an important component of any air quality management programme. Broadly, two approaches are used to predict the ambient air quality – the deterministic and the statistical approach, with each approach having its own merits and demerits. While the models based on the deterministic approach accurately predict the concentrations of air pollutants in the middle percentile range, the statistical models provide a better estimate of concentrations in the extreme percentile ranges. However, the statistical models are site specific and are not able generate ‘what-if’ scenarios; the deterministic models on the other hand are general in character and useful in creating alternative scenarios. An alternative approach – hybrid modelling is a technique which aggregates the benefits of the two techniques and predicts the ‘entire range’ of the distribution. While in the past there were attempts to predict the concentrations of inert pollutants using hybrid modelling approach, this paper shows the hybrid model applications for reactive secondary pollutants like ground level Ozone (GLO). This study presents the development of a hybrid model that concatenates the results of CMAQ (community multi-scale air quality model) as its deterministic component with statistical distribution model (based on the specific area category and timeframe) to predict the entire range of GLO concentrations. Predictions have been made using both purely deterministic and hybrid approaches at a receptor location near a major traffic intersection. The performance of the model has been found to improve from an index of agreement from 0.77 (deterministic model) to 0.91 (hybrid model). In order to assess the predictive capability of the hybrid approach, the model has been tested at an entirely different location for different set of temporal data. The results show an improvement in the predictions using the hybrid model over the deterministic model.

Impact of vehicle fleet characteristics on ambient PM concentrations during rainy season at Farm Gate (CAMS-2) in Dhaka

The impact of vehicle fleet characteristics on ambient particulate matters (both PM2.5 and PM10) concentrations during the rainy season was investigation. Sampling was carried out at the continuous air monitoring station (CAMS-2) at the Farm Gate site, a location near a major traffic intersection. It was observed from the analysis, that the PM2.5 concentrations were not varying so much between day and night time. This might be due to the rainfall and high humidity during these days. As the PM2.5 is less, so BC/PM2.5 becomes high compared to winter season. Therefore PM2.5 /PM10 ratio is also less compared to winter time, as PM2.5 is the part of PM10. The black carbon (BC) value is less during this period compared to winter due to both local and meteorological effect. It was also found that although there was heavy rainfall on 13 July 2010, the PM concentration is also high. It may be due to the contribution of sea salt to PM. Bangladesh J. Sci. Ind. Res. 48(2), 143-150, 2013 DOI: http://dx.doi.org/10.3329/bjsir.v48i2.15746

Diurnal and seasonal variations of carbon monoxide and nitrogen dioxide in Delhi city

Motor vehicle exhaust emissions are one of the major causes of air quality deterioration in most of the cities of the developing world. Carbon monoxide (CO) and nitrogen dioxide (NO2) are significant contributors to this adverse effect on the environment. This study analyses air quality data for three years from 1997 to 1999, at two air quality control regions in Delhi city. The regions are a major traffic intersection and the moderately busy straight Khelgaon Marg road. The data were obtained from the Central Pollution Control Board (CPCB), Delhi. The results show that the highest ground-level concentrations of CO and NO2 occurred during winter (November to March) and the lowest during the tropical monsoon period (July to September) at both regions. Typical average monthly, weekly and diurnal cycles of CO at both regions have also been analysed, and show that CO concentrations are higher at the intersection than along the road. Further, the monthly average NO2 concentrations were also found to be higher at the intersection.

Diurnal and seasonal variations of carbon monoxide and nitrogen dioxide in Delhi city

Motor vehicle exhaust emissions are one of the major causes of air quality deterioration in most of the cities of the developing world. Carbon monoxide (CO) and nitrogen dioxide (NO2) are significant contributors to this adverse effect on the environment. This study analyses air quality data for three years from 1997 to 1999, at two air quality control regions in Delhi city. The regions are a major traffic intersection and the moderately busy straight Khelgaon Marg road. The data were obtained from the Central Pollution Control Board (CPCB), Delhi. The results show that the highest ground-level concentrations of CO and NO2 occurred during winter (November to March) and the lowest during the tropical monsoon period (July to September) at both regions. Typical average monthly, weekly and diurnal cycles of CO at both regions have also been analysed, and show that CO concentrations are higher at the intersection than along the road. Further, the monthly average NO2 concentrations were also found to be higher at the intersection.

Short-term, real-time prediction of the extreme ambient carbon monoxide concentrations due to vehicular exhaust emissions using transfer function-noise model

The Box–Jenkins transfer function-noise (TFN) models (Box, G.E.P., Jenkins, G.M., Reinsel, G.C., 1994. Time Series Analysis: Forecasting and Control, 3rd ed. Prentice-Hall, Englewood Cliffs, NJ.) have been used to provide short-term, real-time forecast of the extreme carbon monoxide for an air quality control region (AQCR) comprising a major traffic intersection in the centre of the capital city of Delhi. The time series of the surface wind speed and ambient temperature have been used as “explaining” exogenous variables in the TFN models. When compared with the results of univariate ARIMA model of the endogenous series, the forecast performance is found to improve with the inclusion of the wind speed as input series; however, no significant improvement is observed in the forecast with the inclusion of temperature as input series.

Real-time prediction of extreme ambient carbon monoxide concentrations due to vehicular exhaust emissions using univariate linear stochastic models

Historical data of the time-series of carbon monoxide (CO) concentration was analysed using Box–Jenkins modelling approach. Univariate Linear Stochastic Models (ULSMs) were developed to examine the degree of prediction possible for situations where only a limited data set, restricted only to the past record of pollutant data are available. The developed models can be used to provide short-term, real-time forecast of extreme CO concentrations for an Air Quality Control Region (AQCR), comprising a major traffic intersection in a Central Business District of Delhi City, India.