Asheesh Sharma

Bio: Asheesh Sharma is an academic researcher from National Environmental Engineering Research Institute. The author has contributed to research in topics: Noise & Traffic noise. The author has an hindex of 7, co-authored 13 publications receiving 135 citations. Previous affiliations of Asheesh Sharma include Council of Scientific and Industrial Research.

Assessment of honking impact on traffic noise in urban traffic environment of Nagpur, India.

Abstract: In context of increasing traffic noise in urban India, the objective of the research study is to assess noise due to heterogeneous traffic conditions and the impact of honking on it. Traffic volume, noise levels, honking, road geometry and vehicular speed were measured on national highway, major and minor roads in Nagpur, India. Initial study showed lack of correlation between traffic volume and equivalent noise due to some factors, later identified as honking, road geometry and vehicular speed. Further, frequency analysis of traffic noise showed that honking contributed an additional 2 to 5 dB (A) noise, which is quite significant. Vehicular speed was also found to increase traffic noise. Statistical method of analysis of variance (ANOVA) confirms that frequent honking (p < 0.01) and vehicular speed (p < 0.05) have substantial impact on traffic noise apart from traffic volume and type of road. The study suggests that honking must also be a component in traffic noise assessment and to identify and monitor “No Honking” zones in urban agglomerations.

High Resolution Urban Air Quality Modeling by Coupling CFD and Mesoscale Models: a Review

Abstract: According to World Health Organization, 9 out of 10 people breathe polluted air and the ambient air pollution accounts for nearly 4.2 million early deaths worldwide. There is an urgent need for scientific management of urban air systems. Mathematical modeling of air quality helps the researchers and urban authorities in devising scientific management plans for mitigation of the associated impacts. We present an organized review of the broad aspects related to urban air quality modeling such as – urban microclimate, geospatial data, chemical transport models, computational fluid dynamics (CFD) models and integration of CFD and mesoscale models. The paper also discusses about the influence of urban land scape features on air quality, accuracy of emission inventory and model validation methods. The present review provides a vantage point to the researchers in the emerging field of high resolution urban air quality modeling for devising the location specific mitigation plans for the scientific management of the clean air.

An adaptive neuro-fuzzy interface system model for traffic classification and noise prediction

Abstract: In present study, two adaptive neuro-fuzzy models have been developed for traffic classification and noise prediction, respectively. The traffic classification model (ANFIS-TC) classifies extracted sound features of different categories of vehicles based on their acoustic signatures. The model also compute total number of vehicles passes through a particular sampling point. The results have been used for the estimation of the equivalent traffic flow ( $$Q_\mathrm{E})$$ . The noise prediction model (ANFIS-TNP) has three inputs, namely equivalent traffic flow ( $$Q_\mathrm{E})$$ , equivalent vehicle speed ( $$S_\mathrm{E})$$ and honking. The equivalent traffic flow ( $$Q_\mathrm{E})$$ is the output of ANFIS-TC model, while equivalent vehicle speed ( $$S_\mathrm{E})$$ and honking are computed from observed averaged speed of different categories of vehicles and number of recorded horns blow per minute. The model assumes that the distance between sound level meter and road centerline is fixed for particular sampling point. The performance of both the models has been validated by field observations. The results show that traffic classification is 100% accurate, while correlation coefficients between observed and predicted traffic noise range from 0.75 to 0.96. Both the models are validated with random samples of data, and it is observed that both the models are generalized and could be employed for traffic classification and traffic noise prediction in small urban heterogeneous traffic environment for noise pollution assessment and control.

Development of a traffic noise prediction model for an urban environment.

Abstract: The objective of this study is to develop a traffic noise model under diverse traffic conditions in metropolitan cities. The model has been developed to calculate equivalent traffic noise based on four input variables i.e. equivalent traffic flow (Q e ), equivalent vehicle speed (S e ) and distance (d) and honking (h). The traffic data is collected and statistically analyzed in three different cases for 15-min during morning and evening rush hours. Case I represents congested traffic where equivalent vehicle speed is 30 km/h and case III represents calm traffic where no honking is recorded. The noise model showed better results than earlier developed noise model for Indian traffic conditions. A comparative assessment between present and earlier developed noise model has also been presented in the study. The model is validated with measured noise levels and the correlation coefficients between measured and predicted noise levels were found to be 0.75, 0.83 and 0.86 for case I, II and III respectively. The noise model performs reasonably well under different traffic conditions and could be implemented for traffic noise prediction at other region as well.

The rise of low-cost sensing for managing air pollution in cities

Abstract: Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

A comparison investigation on photocatalytic activity performance and adsorption efficiency for the removal of cationic dye: Quantum dots vs. magnetic nanoparticles

Abstract: In this research, the efficiencies of two methods, including quantum dots (QDs) based photocatalysis and magnetic nanoparticles (MNPs) based adsorption for the decolorization of Victoria blue R (VBR) were investigated and compared, experimentally. Synthesis of functionalized zinc sulfide (ZnS) QDs and iron oxide (Fe3O4) MNPs was carried out by a simple chemical precipitation method. 2-mercaptoethanol (ME) and sodium dodecyl sulfate (SDS) were applied for capping and surface modification of ZnS QDs and Fe3O4 MNPs, respectively. The prepared nanoparticles have been characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), transmission electron microscopy (TEM). The mean particle size of ZnS QDs and Fe3O4 MNPs were approximated to be 1–3 nm and 50–80 nm, respectively. In the photocatalytic and adsorption investigations, influence of affecting parameters on the removal efficiencies was studied and optimized. According to the results, both methods can be considered as green, simple and efficient strategies for the removal of organic dyes. However, comparative investigation of the characteristics of the methods demonstrated that the efficiency of the QDs based photodegradation method for the removal of VBR is higher than MNPs based adsorption process. It was also found that the maximum decolorization of 95% and 65% can be achieved after 20 and 45 min at optimum pH 10, 7.5, in the presence of 8 and 10 mg of QDs and MNPs for 30 mg/L of VBR, respectively. Finally, isotherm adsorption model and photodegradation mechanism were discussed, too.

Evaluation and analysis of traffic noise along bus rapid transit system corridor

Abstract: Due to increasing motorization, construction of flyovers and growth in transport network, the noise level has exceeded the prescribed limits in many Indian cities. The health implications of high noise levels are being identified as hypertension, sleeplessness, mental stress, etc. Due to this adverse effect of noise level, it is essential to assess the impact of traffic noise on residents and road users. This research is an effort to quantify and analyze the traffic noise emissions along bus rapid transit corridor in Delhi. Field measurements were carried out to understand and assess various aspects of the impact of bus rapid transit system corridor on land use and social lives of residents and road users. The present analysis presents the comparison between observed and predicted noise level at selected corridors and also describes the mitigatory measures to overcome such type of traffic noise pollution through design of noise barrier along the road and motivate people towards the use of public transport system.