Akhil Kadiyala

Study of In-Vehicle Pollutant Variation in Public Transport Buses Operating on Alternative Fuels in the City of Toledo, Ohio

This study focuses on determining the variation of indoor pollutants in public transport buses in the City of Toledo running on biodiesel (BD) and ultra low sulfur diesel (ULSD). The indoor pollutants monitored are carbon dioxide (CO2), carbon monoxide (CO), sulfur dioxide (SO2), nitric oxide (NO), and particulate matter (PM). Temperature (Temp.) and relative humidity (RH) are also measured inside the vehicle in addition to the monitored in-vehicle pollutants. The various factors generally affecting indoor air quality in any microenvironment are indoor sources of pollutants (people, furniture, etc.), ventilation, outdoor air quality, meteorology, pollutant decay, and vehicular traffic. The objective of this research paper is to study the daily, monthly, and seasonal variation of in-vehicle pollutants in relation to different vari- ables and also determine the statistical significance of in-vehicle pollutant levels in biodiesel and ultra low sulfur diesel buses. The daily, monthly, and seasonal variations of the pollutants monitored are studied and it was observed that the pollutant level buildup within a bus compartment is due to a combination of different factors and not a result of variation due to a single variable. CO2 levels are influenced by a combination of varying passenger ridership, vehicular traffic, ventilation settings, and bus status. CO and SO2 levels depend on vehicular traffic, ventilation settings, and to an extent on vehicle speed. NO levels varied with vehicular traffic and ventilation settings. PM levels are influenced by vehicular traffic, ventilation settings and vehicle speed. Relatively higher pollutant concentrations are observed for the majority of pollutants in winter months when there is not much air exchange in the bus compartment. A study of the trends revealed that the concentrations were mainly influenced by peak hours, ventilation settings, vehicular traffic, passenger ridership, and meteorology. The pollut- ant levels of CO2 and SO2 are found to be statistically significantly higher in an ultra low sulfur diesel bus while the pol- lutant levels of CO, NO, and particle numbers with size range between 0.30 � m and 0.40 � m are found to be statistically significantly higher in a biodiesel bus. Particulate matter concentrations are found to be statistically similar in both the test buses.

Development and application of a methodology to identify and rank the important factors affecting in-vehicle particulate matter.

The present study adopted a two-step approach in the development of a methodology to identify and rank the important factors affecting in-vehicle particulate matter (PM). Firstly, the important factors affecting the monitored vehicular PM were identified using regression trees, considering several factors (meteorology, time-related, indoor sources, on-road, and ventilation) that could impact the vehicular indoor air quality. Secondly, the analysis of variance was used as a complementary sensitivity analysis to the regression tree results to rank the significant factors affecting vehicular PM. In-vehicle PM concentrations and sub-micron particle numbers were mainly influenced by the monthly/seasonal changes. Visibility and ambient PM(2.5) additionally influenced the sub-micron particles. Furthermore, this study emphasized the variation of the monitored vehicular PM levels under different combinations of the ranked influential factors.

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3–0.4 µm sized particle numbers, 0.4–0.5 µm sized particle numbers, particulate matter (PM) concentrations less than 1.0 µm (PM1.0), and PM concentrations less than 2.5 µm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks. Implications: The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also. Supplemental Materials: Supplemental materials are available for this paper. Go to the publishers online edition of the Journal of the Air & Waste Management Association.

Quantification of in-vehicle gaseous contaminants of carbon dioxide and carbon monoxide under varying climatic conditions

This study quantifies the monitored in-vehicle contaminants of carbon dioxide (CO2) and carbon monoxide (CO) under varying climatic conditions using advanced statistical methods of regression trees and analysis of variance (ANOVA). The independent influential variables affecting vehicular CO2 and CO are first identified by using regression trees after considering meteorology, monitoring periods, indoor sources, on-road vehicles, and ventilation. Next, ANOVA is used as a complementary analysis to regression tree results to determine the statistical significance of the identified influential variables and to prioritize the statistically significant variables based on the F value. Passenger ridership and month are observed to have a predominant influence on in-vehicle CO2, while month and sky conditions showed a predominant influence on vehicular CO levels. High passenger ridership on a warm/hot day with good ventilation resulted in high CO2 buildup inside the vehicle. High levels of CO are observed inside the vehicle during fall, spring, and summer months on overcast days, with low to medium indoor temperatures, moderate to good ventilating conditions, low indoor relative humidity, and low wind speeds.

Estimation of Uncertainty in Predicting Ground Level Concentrations from Direct Source Releases in an Urban Area Using the USEPA’s AERMOD Model Equations

One of the important prerequisites for a model to be used in decision making is to perform uncertainty and sensitivity analyses on the outputs of the model. This study presents a comprehensive review of the uncertainty and sensitivity analyses associated with prediction of ground level pollutant concentrations using the USEPA’s AERMOD equations for point sources. This is done by first putting together an approximate set of equations that are used in the AERMOD model for the stable boundary layer (SBL) and convective boundary layer (CBL). Uncertainty and sensitivity analyses are then performed by incorporating the equations in Crystal Ball® software. Various parameters considered for these analyses include emission rate, stack exit velocity, stack exit temperature, wind speed, lateral dispersion parameter, vertical dispersion parameter, weighting coefficients for both updraft and downdraft, total horizontal distribution function, cloud cover, ambient temperature, and surface roughness length. The convective mixing height is also considered for the CBL cases because it was specified. The corresponding probability distribution functions, depending on the measured or practical values are assigned to perform uncertainty and sensitivity analyses in both CBL and SBL cases. The results for uncertainty in predicting ground level concentrations at different downwind distances in CBL varied between 67% and 75%, while it ranged between 40% and 47% in SBL. The sensitivity analysis showed that vertical dispersion parameter and total horizontal distribution function have contributed to 82% and 15% variance in predicting concentrations in CBL. In SBL, vertical dispersion parameter and total horizontal distribution function have contributed about 10% and 75% to variance in predicting concentrations respectively. Wind speed has a negative contribution to variance and the other parameters had a negligent or zero contribution to variance. The study concludes that the calculations of vertical dispersion parameter for the CBL case and of horizontal distribution function for the SBL case should be improved to reduce the uncertainty in predicting ground level concentrations. 8

Evaluation of Geographic Information Systems-Based Spatial InterpolationMethods Using Ohio Indoor Radon Data

This paper evaluates the performance of six different Geographic Information System based interpolation methods: inverse distance weighting (IDW), radial basis function (RBF), global polynomial interpolation, local polyno- mial interpolation, kriging, and cokriging, using the Ohio homes database developed between 1987 and 2011. The best performing interpolation method to be used in the prediction of radon gas concentrations in the unmeasured areas of Ohio, USA was determined by validating the model predictions with operational performance measures. Additionally, this study performed a zip code level-based analysis that provided a complete picture of the radon gas concentration distribution in Ohio. The RBF method was identified to be the best performing method. While the RBF method performed significantly better than the IDW, it was statistically similar to the other interpolation methods. The RBF predicted radon gas concentration results indicated a significant increase in the number of zip codes that exceeded the United States Environmental Protec- tion Agency and the World Health Organization action limits, thereby, indicating the need to mitigate the Ohio radon gas concentrations to safe levels in order to reduce the health effects. The approach demonstrated in this paper can be applied to other radon-affected areas around the world.