The Experts below are selected from a list of 1929 Experts worldwide ranked by ideXlab platform
Mukesh Khare - One of the best experts on this subject based on the ideXlab platform.
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performance evaluation of air quality dispersion models at urban intersection of an indian city a case study of delhi city
Artificial Intelligence Review, 2012Co-Authors: Mukesh Khare, S Shiva M Nagendra, Sunil GuliaAbstract:Air quality modelling plays an important role in formulating air pollution control and management strategies by providing guidelines for better and more efficient air quality planning. Several air quality dispersion models are used to evaluate the urban air quality. The performance and efficiency of an air quality model are mainly depends upon the accurately interpretations of the complex interactions between various atmospheric, emission and topographic parameters involved in the air pollution problem. In this paper, four state-of art air quality models like AERMOD, ADMS- Urban, ISCST3 and CALINE4 and two codes i.e. GFLSM and DFLSM (based on Gaussian principle) have been used to predict the air quality of an urban intersection of Delhi city, India, followed by their performance evaluation. These models are applied to predict the concentration of Carbon monoxide (CO), Nitrogen dioxide (NO2) and PM2.5 (size less than 2.5 micron) which are one of the major components of Vehicular Exhaust emissions. The performance of all models/codes have been evaluated using standard statistical descriptor like Index of Agreement (d), Factor of 2 (FAC2), Fractional Bias (FB), Normalized Mean Square Error (NMSE), Geometric Mean Bias and Geometric Mean Variance. The index of agreement (d) value for CO concentration indicates that ISCST3 model (d=0.69) performs satisfactorily when compared with AERMOD (d=0.50) and ADMS-Urban (d=0.45) for winter period. The performances of CALINE 4, DFLSM and GFLSM have been observed not satisfactory having d values less than 0.4. Further, the ADMS – urban has performed satisfactorily in predicting
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construction of fuzzy membership functions for urban Vehicular Exhaust emissions modeling
Environmental Monitoring and Assessment, 2010Co-Authors: Suresh Jain, Mukesh KhareAbstract:This paper presents a method for constructing a membership function (MF) for the fuzzy sets that expert systems deal with. This paper introduces a Bezier curve-based mechanism for constructing MFs of convex normal fuzzy sets. The mechanism can fit any given data set with a minimum level of discrepancy. In the absence of data, the mechanism can be intuitively manipulated by the user to construct MFs with the desired shape. MFs have been developed using the proposed mechanism for urban Vehicular Exhaust emission modeling. It has been observed that all meteorological and Vehicular parameters have either S-shaped MFs or Z-shaped MFs. Gaussian MF has been mostly applied for modeling air quality. The present study explored the application of fuzzy MF to analyze air pollution data from Vehicular emission. The study reveals that S-shaped and Z-shaped MF can be used in addition to Gaussian MF.
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artificial neural network approach for modelling nitrogen dioxide dispersion from Vehicular Exhaust emissions
Ecological Modelling, 2006Co-Authors: S Shiva M Nagendra, Mukesh KhareAbstract:Abstract Artificial neural networks (ANNs) are useful alternative techniques in modelling the complex Vehicular Exhaust emission (VEE) dispersion phenomena. This paper describes a step-by-step procedure to model the nitrogen dioxide (NO 2 ) dispersion phenomena using the ANN technique. The ANN-based NO 2 models are developed at two air-quality-control regions (AQCRs), one, representing, a traffic intersection (AQCR1) and the other, an arterial road (AQCR2) in the Delhi city. The models are unique in the sense that they are developed for ‘heterogeneous 1 ’ traffic conditions and tropical meteorology. The inputs to the model consist of 10 meteorological and 6 traffic characteristic variables. Two-year data, from 1 January 1997 to 31 December 1998 has been used for model training and data from 1 January to 31 December 1999, for model testing and evaluation purposes. The results show satisfactory performance of the ANN-based NO 2 models on the evaluation data set at both the AQCRs ( d = 0.76 for AQCR1, and d = 0. 59 for AQCR2).
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artificial neural network based line source models for Vehicular Exhaust emission predictions of an urban roadway
Transportation Research Part D-transport and Environment, 2004Co-Authors: S Shiva M Nagendra, Mukesh KhareAbstract:Abstract The dispersion characteristics of Vehicular Exhaust emissions on urban roadways are highly non-linear and the presence of `traffic wake' adds complexities to the dispersion. Gaussian deterministic line source models may not then be able to explain variations in related meteorological and traffic characteristic variables. Artificial neural networks comprising of interconnected adaptive processing units have the capability to recognize the non-linearity present in incomplete or noisy data. One-hour average artificial neural network based carbon monoxide models are developed for two air quality control regions in Delhi city––a traffic intersection and an arterial road. Ten meteorological and six traffic characteristic variables are used in the model. The results demonstrate that neural network models are able to explain the effects of `traffic wake' on the CO dispersion in the near field regions of a roadway.
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a review of deterministic stochastic and hybrid Vehicular Exhaust emission models
International Journal of Transport Management, 2004Co-Authors: Sharad Gokhale, Mukesh KhareAbstract:Vehicles spend more time near junctions and intersections in different driving modes, i.e., queuing, decelerating or accelerating and thus generating more pollutants than at road links [Claggett, M., Shrock, J., Noll, K.E., 1981. Carbon monoxide near an urban intersection. Atmos. Environ. 15, 1633–1642]. As a result, the receptors in these urban corridors are prone to frequent exposures of high pollutant concentrations (episodic conditions). In order to predict such ‘episodes’, an air quality model, capable of estimating the entire range (middle and extremes) of pollutant concentration distribution is needed. Hybrid models (combining deterministic and statistical distribution models) have demonstrated the ability to predict the entire range of pollutant concentrations in such co mplex dispersion situations with reasonable accuracy [Jakeman, A., Simpson, R.W., Taylor, J.A., 1988. Modelling distributions of air pollutant concentrations-III: Hybrid modelling deterministic-statistical distributions. Atmos. Environ. 22 (1) 163–174]. The present paper reviews the relevant deterministic and stochastic based Vehicular Exhaust emission models that may be hybridized and thus generate a hybrid model with improved prediction accuracy. The paper also describes the implications of hybrid models in formulating the Episodic-Urban Air Quality Management Plan (e-UAQMP).
S Shiva M Nagendra - One of the best experts on this subject based on the ideXlab platform.
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modal analysis of real time real world Vehicular Exhaust emissions under heterogeneous traffic conditions
Transportation Research Part D-transport and Environment, 2017Co-Authors: Rohit Jaikumar, S Shiva M Nagendra, R SivanandanAbstract:Abstract This study presents the characteristics of real world, real time, on-road Vehicular Exhaust emission namely, carbon monoxide (CO), nitric oxide (NO), hydrocarbons (HC), and carbon dioxide (CO 2 ) emitted under heterogeneous traffic conditions. Field experiments were performed on major category of vehicles in developing countries, i.e. two-wheelers, auto-rickshaws, cars and buses. The on-board monitoring was carried out on different corridors with varying road geometry. Results revealed that the driving cycle was dependent on the road geometry, with two lane mixed flow corridor having lot of short term events compared to that of arterial road. Vehicular emissions during idling and cruising were generally low compared to emissions during acceleration. It was also found that emissions were significantly dependent on short term events such as rapid acceleration and braking during a trip. Also, the standard emission models like COPERT and CMEM under predicted the real world emissions by 30–200% depending upon different driving modes. The on-road emissions measurements were able to capture the emission characteristics during the micro events of real world driving scenarios which were not represented by standard vehicle emission measured at laboratory conditions.
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performance evaluation of air quality dispersion models at urban intersection of an indian city a case study of delhi city
Artificial Intelligence Review, 2012Co-Authors: Mukesh Khare, S Shiva M Nagendra, Sunil GuliaAbstract:Air quality modelling plays an important role in formulating air pollution control and management strategies by providing guidelines for better and more efficient air quality planning. Several air quality dispersion models are used to evaluate the urban air quality. The performance and efficiency of an air quality model are mainly depends upon the accurately interpretations of the complex interactions between various atmospheric, emission and topographic parameters involved in the air pollution problem. In this paper, four state-of art air quality models like AERMOD, ADMS- Urban, ISCST3 and CALINE4 and two codes i.e. GFLSM and DFLSM (based on Gaussian principle) have been used to predict the air quality of an urban intersection of Delhi city, India, followed by their performance evaluation. These models are applied to predict the concentration of Carbon monoxide (CO), Nitrogen dioxide (NO2) and PM2.5 (size less than 2.5 micron) which are one of the major components of Vehicular Exhaust emissions. The performance of all models/codes have been evaluated using standard statistical descriptor like Index of Agreement (d), Factor of 2 (FAC2), Fractional Bias (FB), Normalized Mean Square Error (NMSE), Geometric Mean Bias and Geometric Mean Variance. The index of agreement (d) value for CO concentration indicates that ISCST3 model (d=0.69) performs satisfactorily when compared with AERMOD (d=0.50) and ADMS-Urban (d=0.45) for winter period. The performances of CALINE 4, DFLSM and GFLSM have been observed not satisfactory having d values less than 0.4. Further, the ADMS – urban has performed satisfactorily in predicting
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artificial neural network approach for modelling nitrogen dioxide dispersion from Vehicular Exhaust emissions
Ecological Modelling, 2006Co-Authors: S Shiva M Nagendra, Mukesh KhareAbstract:Abstract Artificial neural networks (ANNs) are useful alternative techniques in modelling the complex Vehicular Exhaust emission (VEE) dispersion phenomena. This paper describes a step-by-step procedure to model the nitrogen dioxide (NO 2 ) dispersion phenomena using the ANN technique. The ANN-based NO 2 models are developed at two air-quality-control regions (AQCRs), one, representing, a traffic intersection (AQCR1) and the other, an arterial road (AQCR2) in the Delhi city. The models are unique in the sense that they are developed for ‘heterogeneous 1 ’ traffic conditions and tropical meteorology. The inputs to the model consist of 10 meteorological and 6 traffic characteristic variables. Two-year data, from 1 January 1997 to 31 December 1998 has been used for model training and data from 1 January to 31 December 1999, for model testing and evaluation purposes. The results show satisfactory performance of the ANN-based NO 2 models on the evaluation data set at both the AQCRs ( d = 0.76 for AQCR1, and d = 0. 59 for AQCR2).
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artificial neural network based line source models for Vehicular Exhaust emission predictions of an urban roadway
Transportation Research Part D-transport and Environment, 2004Co-Authors: S Shiva M Nagendra, Mukesh KhareAbstract:Abstract The dispersion characteristics of Vehicular Exhaust emissions on urban roadways are highly non-linear and the presence of `traffic wake' adds complexities to the dispersion. Gaussian deterministic line source models may not then be able to explain variations in related meteorological and traffic characteristic variables. Artificial neural networks comprising of interconnected adaptive processing units have the capability to recognize the non-linearity present in incomplete or noisy data. One-hour average artificial neural network based carbon monoxide models are developed for two air quality control regions in Delhi city––a traffic intersection and an arterial road. Ten meteorological and six traffic characteristic variables are used in the model. The results demonstrate that neural network models are able to explain the effects of `traffic wake' on the CO dispersion in the near field regions of a roadway.
Wu R. R. - One of the best experts on this subject based on the ideXlab platform.
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Characterization of ambient volatile organic compounds and their sources in Beijing, before, during, and after Asia-Pacific Economic Cooperation China 2014
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2015Co-Authors: Li J., Xie S. D., Zeng L. M., Li L. Y., Li Y. Q., Wu R. R.Abstract:Ambient volatile organic compounds (VOCs) were measured using an online system, gas chromatography-mass spectrometry/flame ionization detector (GC-MS/FID), in Beijing, China, before, during, and after Asia-Pacific Economic Cooperation (APEC) China 2014, when stringent air quality control measures were implemented. Positive matrix factorization (PMF) was applied to identify the major VOC contributing sources and their temporal variations. The secondary organic aerosols potential (SOAP) approach was used to estimate variations of precursor source contributions to SOA formation. The average VOC mixing ratios during the three periods were 86.17, 48.28, and 72.97 ppbv, respectively. The mixing ratios of total VOC during the control period were reduced by 44 %, and the mixing ratios of acetonitrile, halocarbons, oxygenated VOCs (OVOCs), aromatics, acetylene, alkanes, and alkenes decreased by approximately 65, 62, 54, 53, 37, 36, and 23 %, respectively. The mixing ratios of all measured VOC species decreased during control, and the most affected species were chlorinated VOCs (chloroethane, 1,1-dichloroethylene, chlorobenzene). PMF analysis indicated eight major sources of ambient VOCs, and emissions from target control sources were clearly reduced during the control period. Compared with the values before control, contributions of Vehicular Exhaust were most reduced, followed by industrial manufacturing and solvent utilization. Reductions of these three sources were responsible for 50, 26, and 16% of the reductions in ambient VOCs. Contributions of evaporated or liquid gasoline and industrial chemical feedstock were slightly reduced, and contributions of secondary and long-lived species were relatively stable. Due to central heating, emissions from fuel combustion kept on increasing during the whole campaign; because of weak control of liquid petroleum gas (LPG), the highest emissions of LPG occurred in the control period. Vehicle-related sources were the most important precursor sources likely responsible for the reduction in SOA formation during this campaign.Environmental Protection Ministry of China for Research of Characteristics and Controlling Measures of VOC Emissions [2011467003, 20130973]SCI(E)ARTICLEsdxie@pku.edu.cn147945-79591
Markku Kulmala - One of the best experts on this subject based on the ideXlab platform.
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correction to modelling of the influence of aerosol processes for the dispersion of Vehicular Exhaust plumes in street environment
Atmospheric Environment, 2006Co-Authors: Mia Pohjola, Liisa Pirjola, Jaakko Kukkonen, Markku KulmalaAbstract:Abstract A paper that was previously published in Atmospheric Environment [Pohjola et al., 2003. Modelling of the influence of aerosol processes for the dispersion of Vehicular Exhaust plumes in street environment. Atmospheric Environment 37(3), 339–351.] contained a description of the properties of particulate matter in Exhaust emissions and in urban background air. This data contained particle number concentrations and particle masses for four size classes. The mass and number concentrations of particulate matter Exhaust emissions were computed correctly. However, a subsequent checking has shown that the values of particle masses of urban background air that were used in the numerical computations were one to two orders of magnitude too small, in relation to the corresponding particle number concentrations of urban background air. This has affected the numerical results on particle sizes, and produced an erroneous temporally decreasing particle size in some cases; this is notable particularly for the Aitken mode. The calculations have therefore been repeated, replacing the erroneous data with the correct data. However, the main conclusions drawn by Pohjola et al. (2003) are shown still to be valid, using the corrected input data.
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modelling of the influence of aerosol processes for the dispersion of Vehicular Exhaust plumes in street environment
Atmospheric Environment, 2003Co-Authors: Mia Pohjola, Liisa Pirjola, Jaakko Kukkonen, Markku KulmalaAbstract:Abstract We have analysed the influence of various aerosol dynamical processes and plume dilution on the properties of Vehicular Exhaust particulate matter. A monodisperse aerosol process model MONO32 with four size modes was applied for evaluating the number concentration, size distribution and chemical composition of the particles during a time period of 25 s, after the particles were emitted from the Exhaust pipe. The model takes into account aerosol dynamics and gas-phase chemistry including emissions of gases and particles, chemical reactions in the gas phase, dry deposition of particles and gases, homogenous binary H2SO4–H2O or ternary H2SO4–H2O–NH3 nucleation, multicomponent condensation of H2SO4, H2O and some organic vapour onto the particles, inter- and intra-mode coagulation of particles, and plume dilution with the background air. The dilution parameters are calculated by a simple plume model. Numerical computations were performed assuming specific Vehicular Exhausts under an urban environment, characteristic for the Helsinki Metropolitan Area, in summer. The results showed that condensation of an insoluble organic vapour is important under the selected conditions, if its concentration exceeds a threshold value of 1010 or 1011 cm–3 for the Aitken and accumulation mode particles, respectively. The condensation or evaporation of water can also be an important process; however, its influence is strongly dependent on the hygroscopicity of particles. The effect of coagulation is substantial only, if the dilution of the Exhaust plume is neglected. After the computational time of 25 s, the particulate population reaches so-called quasi-equilibrium state, i.e., most of the particulate matter transformation processes have already taken place within this time period.
Sharad Gokhale - One of the best experts on this subject based on the ideXlab platform.
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impacts of traffic flows on Vehicular Exhaust emissions at traffic junctions
Transportation Research Part D-transport and Environment, 2012Co-Authors: Sharad GokhaleAbstract:This paper examines the impact of traffic-flow on CO, NO2 and PM emissions at two distinct traffic junctions and evaluates the use of emission factors. The study includes three scenarios regarding pollutant emissions, which combine a field, experimental and semi-empirically estimated traffic parameters for free, interrupted and congested traffic-flow conditions. It evaluates the emission patterns for heterogeneity in traffic characteristics of both junctions. The results suggest the corrections to be made to emission factors at traffic junctions for better forecast of air quality.
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evaluating effects of traffic and vehicle characteristics on Vehicular emissions near traffic intersections
Transportation Research Part D-transport and Environment, 2009Co-Authors: Suresh Pandian, Sharad Gokhale, Aloke Kumar GhoshalAbstract:Abstract Urban air quality is generally poor at traffic intersections due to variations in vehicles’ speeds as they approach and leave. This paper examines the effect of traffic, vehicle and road characteristics on Vehicular emissions with a view to understand a link between emissions and the most likely influencing and measurable characteristics. It demonstrates the relationships of traffic, vehicle and intersection characteristics with Vehicular Exhaust emissions and reviews the traffic flow and emission models. Most studies have found that Vehicular Exhaust emissions near traffic intersections are largely dependent on fleet speed, deceleration speed, queuing time in idle mode with a red signal time, acceleration speed, queue length, traffic-flow rate and ambient conditions. The Vehicular composition also affects emissions. These parameters can be quantified and incorporated into the emission models. There is no validated methodology to quantify some non-measurable parameters such as driving behaviour, pedestrian activity, and road conditions
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modelling the size separated particulate matter sspm10 from Vehicular Exhaust at traffic intersections in mumbai
Environmental Monitoring and Assessment, 2004Co-Authors: Sharad Gokhale, Rashmi S PatilAbstract:The study was carried out to predict the size separated particulate matter below 10 μm size (SSPM10) from Vehicular Exhausts at traffic intersections using modified general finite line source model (GFLSM). Two air quality control regions (AQCRs) were selected in Mumbai City for this study. One was industrial area (AQCR1) containing the busy intersection, i.e. Marol link road, with the heavy inflow of two-three wheelers. And, the other was commercial busy district area (AQCR2) containing the busy intersection, i.e. Dadar circle, with a heavy traffic flow especially cars. The model was applied at both the traffic intersections. The data were collected for modelling study for three winter months in 1995 using cascade impactor of nine size ranges. The prediction results revealed that modified GFLSM underpredicted the SSPM10 concentrations for all the size ranges. However, showed considerable correlation between observed and predicted values for the size range below 4.7 μm at both the intersections. The relative high concentrations observed in the coarser range of 10–4.7 μm are attributed to the resuspension of the roadside particulate matter. Hence, the amount of underprediction was more for this range, which was due to the characteristics of model that does not take into account the factor for resuspension of roadside particulate matter caused by traffic movements.The model was also applied to predict the total particulate matter for downwind distances from the road intersection. The statistical evaluation of model was done, which indicated that the model's performance was good for the finer range of particles (below 4.7 μm) with r-square values of 0.49 and 0.57 found at both the intersections in AQCR1 and AQCR2, respectively. However, it is not unusual that the model uncertainty is likely to exist due to data input errors and stochastic fluctuations irrespective of the models accurateness. The statistical distribution model was therefore identified using Kolmogorov-Smirnov test. At both the intersections, SSPM10 concentration data were found lognormally distributed.
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a review of deterministic stochastic and hybrid Vehicular Exhaust emission models
International Journal of Transport Management, 2004Co-Authors: Sharad Gokhale, Mukesh KhareAbstract:Vehicles spend more time near junctions and intersections in different driving modes, i.e., queuing, decelerating or accelerating and thus generating more pollutants than at road links [Claggett, M., Shrock, J., Noll, K.E., 1981. Carbon monoxide near an urban intersection. Atmos. Environ. 15, 1633–1642]. As a result, the receptors in these urban corridors are prone to frequent exposures of high pollutant concentrations (episodic conditions). In order to predict such ‘episodes’, an air quality model, capable of estimating the entire range (middle and extremes) of pollutant concentration distribution is needed. Hybrid models (combining deterministic and statistical distribution models) have demonstrated the ability to predict the entire range of pollutant concentrations in such co mplex dispersion situations with reasonable accuracy [Jakeman, A., Simpson, R.W., Taylor, J.A., 1988. Modelling distributions of air pollutant concentrations-III: Hybrid modelling deterministic-statistical distributions. Atmos. Environ. 22 (1) 163–174]. The present paper reviews the relevant deterministic and stochastic based Vehicular Exhaust emission models that may be hybridized and thus generate a hybrid model with improved prediction accuracy. The paper also describes the implications of hybrid models in formulating the Episodic-Urban Air Quality Management Plan (e-UAQMP).