This page has only limited features, please log in for full access.
The Gaussian-based dispersion model American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) is being used to predict concentration for air quality management in several countries. A study was conducted for an industrial area, Chembur of Mumbai city in India, to assess the agreement of observed surface meteorology and weather research and forecasting (WRF) output through AERMOD with ground-level NOx and PM10 concentrations. The model was run with both meteorology and emission inventory. When results were compared, it was observed that the air quality predictions were better with the use of WRF output data for a model run than with the observed meteorological data. This study showed that the onsite meteorological data can be generated by WRF which saves resources and time, and it could be a good option in low-middle income countries (LIMC) where meteorological stations are not available. Also, this study quantifies the source contribution in the ambient air quality for the region. NOx and PM10 emission loads were always observed to be high from the industries but NOx concentration was high from vehicular sources and PM10 concentration was high from industrial sources in ambient concentration. This methodology can help the regulatory authorities to develop control strategies for air quality management in LIMC.
Awkash Kumar; Anil Dikshit; Rashmi Patil. Use of Simulated and Observed Meteorology for Air Quality Modeling and Source Ranking for an Industrial Region. Sustainability 2021, 13, 4276 .
AMA StyleAwkash Kumar, Anil Dikshit, Rashmi Patil. Use of Simulated and Observed Meteorology for Air Quality Modeling and Source Ranking for an Industrial Region. Sustainability. 2021; 13 (8):4276.
Chicago/Turabian StyleAwkash Kumar; Anil Dikshit; Rashmi Patil. 2021. "Use of Simulated and Observed Meteorology for Air Quality Modeling and Source Ranking for an Industrial Region." Sustainability 13, no. 8: 4276.
A residential-cum-commercial property consisting of seven buildings with approximately 50 m height in Mumbai city of India was considered for disaster management study. The main objective of this study was to minimize all the losses of any kind of possible disasters like fire, lighting, cyclone, flood, earthquake and terrorist attack. Each disaster was imposed on the project design and responses were formulated in order to minimize the damage to lives and resources. Public address systems and alarm systems have been installed at the project site to alert the residents about the disasters and to give directions which are to be followed. Fire mitigation and detection equipment are also present at the project site. In this study, accessibility of the fire tender from each side of the buildings has been analyzed. Assembly points for emergencies have been designated for the project. Lightning arrestors have been installed for protection against lightning strikes. Natural drainage network within 1 km radius from the project site has been analyzed using geographic information system to prevent the. CCTV for security, signages for evacuation plan and rescue equipment have been installed throughout the project site to save lives in priority. The key findings are the losses due to disasters could be minimized with proper planning which might have cost for execution, operation and maintenance. This comprehensive study can be referred for planning to minimize the loss of lives and resources in high-rise buildings in case of disasters in urban area.
Awkash Kumar; Yash Masane; Shraddha Dhakhwa. Disaster management plan for residential-cum-commercial property in Mumbai: a case study. Environment, Development and Sustainability 2021, 1 -17.
AMA StyleAwkash Kumar, Yash Masane, Shraddha Dhakhwa. Disaster management plan for residential-cum-commercial property in Mumbai: a case study. Environment, Development and Sustainability. 2021; ():1-17.
Chicago/Turabian StyleAwkash Kumar; Yash Masane; Shraddha Dhakhwa. 2021. "Disaster management plan for residential-cum-commercial property in Mumbai: a case study." Environment, Development and Sustainability , no. : 1-17.
Awkash Kumar; Rashmi S. Patil; Anil Kumar Dikshit; Rakesh Kumar. Assessment of Spatial Ambient Concentration of NH3 and its Health Impact for Mumbai City. Asian Journal of Atmospheric Environment 2019, 13, 11 -19.
AMA StyleAwkash Kumar, Rashmi S. Patil, Anil Kumar Dikshit, Rakesh Kumar. Assessment of Spatial Ambient Concentration of NH3 and its Health Impact for Mumbai City. Asian Journal of Atmospheric Environment. 2019; 13 (1):11-19.
Chicago/Turabian StyleAwkash Kumar; Rashmi S. Patil; Anil Kumar Dikshit; Rakesh Kumar. 2019. "Assessment of Spatial Ambient Concentration of NH3 and its Health Impact for Mumbai City." Asian Journal of Atmospheric Environment 13, no. 1: 11-19.
Air quality modelling can be a strong tool for air quality management. In the present study, the Danish Urban Background Model (UBM) and the USEPA AERMOD are applied for calculating NOx and total particulate matter (TPM) concentrations for Mumbai city of India for the years 2007 and 2012. In order to compare the results from the two models, two sets of simulations are performed using the same sets of input data for boundary conditions, emissions, and meteorology. The results showed that the NOx calculations from the UBM model were in better agreement with observed data when compared with similar results from the AERMOD model. However, the opposite was the case for TPM for which the results from the AERMOD model were in better agreement with observed data when compared with the results from the UBM. The concentration levels for 2012 were generally higher than for 2007, reflecting differences in meteorological conditions for the 2 years. When comparing the obtained model results with measurements, it should be noted, that the emission inventories have various shortcomings, and that the boundary conditions from the DEHM (Danish Eulerian Hemispheric Model) are obtained with a coarse resolution of 150 km × 150 km. One of the main shortcomings of the TPM emission inventories is that it is not accounted for all the sources. Moreover, for both TPM and NOx, the boundaries of model calculations of the UBM and AERMOD model domain are underestimating the actual concentrations due to the relatively coarse resolution. When the UBM model calculations are scaled to fit the level of the observed concentrations, it is evident that spatial and temporal variation reproduced better results when compared with the results obtained from AERMOD.
Awkash Kumar; Jørgen Brandt; Matthias Ketzel; Rashmi S. Patil; Anil Kumar Dikshit; Ole Hertel. Evaluation of the Urban Background Model (UBM) and AERMOD for Mumbai City. Environmental Modeling & Assessment 2018, 24, 75 -86.
AMA StyleAwkash Kumar, Jørgen Brandt, Matthias Ketzel, Rashmi S. Patil, Anil Kumar Dikshit, Ole Hertel. Evaluation of the Urban Background Model (UBM) and AERMOD for Mumbai City. Environmental Modeling & Assessment. 2018; 24 (1):75-86.
Chicago/Turabian StyleAwkash Kumar; Jørgen Brandt; Matthias Ketzel; Rashmi S. Patil; Anil Kumar Dikshit; Ole Hertel. 2018. "Evaluation of the Urban Background Model (UBM) and AERMOD for Mumbai City." Environmental Modeling & Assessment 24, no. 1: 75-86.
Air quality modeling requires three types of input data viz. emission, meteorology and geographical information and it can help to distinguish the influence of these factors for air pollution. In this present study, a constant emission has been considered for a region and it has been applied in vehicular pollution modeling with various averaging time period of seasons for the year. Chembur, the most polluted area of Mumbai city (India) due to industrial and vehicular sources, has been selected for this study. Generally, temporal and spatial interpolated meteorological data are used in air quality modeling, which is collected from a nearby meteorological station. In this paper, AERMOD was processed with onsite meteorological data, derived from Weather Research and Forecasting (WRF) model. It was applied for a 1 day period and 1 month of winter and monsoon season and again for whole year 2011. The results of AERMOD showed interesting behavior of the model for different averaging times. There is a general understanding in air quality modeling that concentration decreases with increase in averaging time. In this study, the results show that the concentration decreases with increasing of averaging time in winter season while concentration increases with increasing of averaging time period in monsoon season. Also, WRF model has been used for simulating for a year successfully which saves enormous time and resources of collecting meteorological data from a station and gives good result.
Awkash Kumar; Rashmi S. Patil; Anil Kumar Dikshit. Impact of seasonal meteorology and averaging time on vehicular pollution modeling. International Journal of System Assurance Engineering and Management 2017, 8, 1937 -1944.
AMA StyleAwkash Kumar, Rashmi S. Patil, Anil Kumar Dikshit. Impact of seasonal meteorology and averaging time on vehicular pollution modeling. International Journal of System Assurance Engineering and Management. 2017; 8 (2):1937-1944.
Chicago/Turabian StyleAwkash Kumar; Rashmi S. Patil; Anil Kumar Dikshit. 2017. "Impact of seasonal meteorology and averaging time on vehicular pollution modeling." International Journal of System Assurance Engineering and Management 8, no. 2: 1937-1944.
Megacities in India such as Mumbai and Delhi are among the most polluted places in the world. In the present study, the widely used operational street pollution model (OSPM) is applied for assessing pollutant loads in the street canyons of Chembur, a suburban area just outside Mumbai city. Chembur is both industrialized and highly congested with vehicles. There are six major street canyons in this area, for which modeling has been carried out for NOx and particulate matter (PM). The vehicle emission factors for Indian cities have been developed by Automotive Research Association of India (ARAI) for PM, not specifically for PM10 or PM2.5. The model has been applied for 4 days of winter season and for the whole year to see the difference of effect of meteorology. The urban background concentrations have been obtained from an air quality monitoring station. Results have been compared with measured concentrations from the routine monitoring performed in Mumbai. NOx emissions originate mainly from vehicles which are ground-level sources and are emitting close to where people live. Therefore, those emissions are highly relevant. The modeled NOx concentration compared satisfactorily with observed data. However, this was not the case for PM, most likely because the emission inventory did not contain emission terms due to resuspended particulate matter.
Awkash Kumar; Matthias Ketzel; Rashmi S. Patil; Anil Kumar Dikshit; Ole Hertel. Vehicular pollution modeling using the operational street pollution model (OSPM) for Chembur, Mumbai (India). Environmental Monitoring and Assessment 2016, 188, 1 -10.
AMA StyleAwkash Kumar, Matthias Ketzel, Rashmi S. Patil, Anil Kumar Dikshit, Ole Hertel. Vehicular pollution modeling using the operational street pollution model (OSPM) for Chembur, Mumbai (India). Environmental Monitoring and Assessment. 2016; 188 (6):1-10.
Chicago/Turabian StyleAwkash Kumar; Matthias Ketzel; Rashmi S. Patil; Anil Kumar Dikshit; Ole Hertel. 2016. "Vehicular pollution modeling using the operational street pollution model (OSPM) for Chembur, Mumbai (India)." Environmental Monitoring and Assessment 188, no. 6: 1-10.
Air pollution is caused by variety of sources such as industries, vehicles, cremation, bakeries, and open burning. These sources have variation in emission with different time scales. Industry and bakeries have variation in emission with day or week, rest of the sources like vehicles and domestic sector have variation with time in a day. In fact, vehicles have a large variation in emission with time period of the day. The average concentration of 24 h is much less than hourly concentration of peak time when there is heavy vehicular emissions. The hourly concentration of off-peak time or lean time is very low due to low emission for that period. The air quality standards of India are prescribed for 24-h average concentration with which the predicted average concentration from models is compared. However, the peak time concentration may be much higher than the standard. In the peak time, outdoor concentration is more and since a large proportion of the population is out the exposure is also very high and can cause severe health effect. In this paper, vehicular pollution modeling has been carried out using AERMOD with simulated meteorology by Weather Research and Forecasting model. NOx and PM concentrations were 3.6 and 1.45 times higher in peak time than off-peak and evening peak, respectively. Lean time has higher concentration for both NOx and PM than off-peak and evening peak. It shows the misleading concept of comparing average predicted concentration of 24 h with standards for vehicles.
Awkash Kumar; Rashmi S. Patil; Anil Kumar Dikshit; Rakesh Kumar. Comparison of predicted vehicular pollution concentration with air quality standards for different time periods. Clean Technologies and Environmental Policy 2016, 18, 2293 -2303.
AMA StyleAwkash Kumar, Rashmi S. Patil, Anil Kumar Dikshit, Rakesh Kumar. Comparison of predicted vehicular pollution concentration with air quality standards for different time periods. Clean Technologies and Environmental Policy. 2016; 18 (7):2293-2303.
Chicago/Turabian StyleAwkash Kumar; Rashmi S. Patil; Anil Kumar Dikshit; Rakesh Kumar. 2016. "Comparison of predicted vehicular pollution concentration with air quality standards for different time periods." Clean Technologies and Environmental Policy 18, no. 7: 2293-2303.
Industrial air pollution creates severe problems and evaluation of control scenarios rationally which can be carried out using air quality models. In this study, an industrial region Chembur in Mumbai city was selected to estimate air quality change for various control scenarios. Weather Research and Forecasting (WRF) and AMS/EPA Regulatory Model (AERMOD) were used for this study. Since, the industries of Chembur region were already existing and residential and commercial zones were pre-set in the domain, so scenarios like increment of stack height and fuel change of industries can help to make better and healthier air quality. Scenarios selected were the 10% (SH1), 25% (SH2) and 50% (SH3) increment of height of stacks of industry and use of low emission fuel (F1 and F2) in the industries. Industrial air pollution modelling was done for current scenario as well as proposed control scenarios. The reduction of air quality level for each control scenario was calculated. Overall scenario SH3 as expected had more or less maximum reduction at ground level concentration for various temporal cases for all pollutants. However, it would cause more residence time of pollutants in atmosphere. Also this scenario was not based on source emission reduction strategy. Scenario F1 considers fuel with lowest emission and source emission reduction strategy. Therefore, scenario F1 seems to be the most preferred option for all pollutants among all scenarios. This study can help in taking objective and rational decisions for control scenarios for industrial sources.
Awkash Kumar; Rashmi S. Patil; Anil Kumar Dikshit; Sahidul Islam; Rakesh Kumar. Evaluation of control strategies for industrial air pollution sources using American Meteorological Society/Environmental Protection Agency Regulatory Model with simulated meteorology by Weather Research and Forecasting Model. Journal of Cleaner Production 2016, 116, 110 -117.
AMA StyleAwkash Kumar, Rashmi S. Patil, Anil Kumar Dikshit, Sahidul Islam, Rakesh Kumar. Evaluation of control strategies for industrial air pollution sources using American Meteorological Society/Environmental Protection Agency Regulatory Model with simulated meteorology by Weather Research and Forecasting Model. Journal of Cleaner Production. 2016; 116 ():110-117.
Chicago/Turabian StyleAwkash Kumar; Rashmi S. Patil; Anil Kumar Dikshit; Sahidul Islam; Rakesh Kumar. 2016. "Evaluation of control strategies for industrial air pollution sources using American Meteorological Society/Environmental Protection Agency Regulatory Model with simulated meteorology by Weather Research and Forecasting Model." Journal of Cleaner Production 116, no. : 110-117.
Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO2), nitrogen dioxide (NO2) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution.Implications: Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million
Awkash Kumar; Indrani Gupta; Jørgen Brandt; Rakesh Kumar; Anil Kumar Dikshit; Rashmi S. Patil. Air quality mapping using GIS and economic evaluation of health impact for Mumbai City, India. Journal of the Air & Waste Management Association 2016, 66, 470 -481.
AMA StyleAwkash Kumar, Indrani Gupta, Jørgen Brandt, Rakesh Kumar, Anil Kumar Dikshit, Rashmi S. Patil. Air quality mapping using GIS and economic evaluation of health impact for Mumbai City, India. Journal of the Air & Waste Management Association. 2016; 66 (5):470-481.
Chicago/Turabian StyleAwkash Kumar; Indrani Gupta; Jørgen Brandt; Rakesh Kumar; Anil Kumar Dikshit; Rashmi S. Patil. 2016. "Air quality mapping using GIS and economic evaluation of health impact for Mumbai City, India." Journal of the Air & Waste Management Association 66, no. 5: 470-481.
Vehicular pollution is becoming significant in urban areas because of increasing population. This is at ground level, so it gives high population exposure. In this study, Chembur, which is the most polluted area in Mumbai city due to industrial and vehicular sources, is selected for vehicular pollution modeling using AMS/EPA Regulatory Model (AERMOD). Meteorological parameters, land use surface characteristics and source emission data are collected as required by AERMOD. The results of modelling depend upon reliability of input data and meteorological data has a vital role in the performance of the model. Generally, temporally and spatially interpolated meteorological data is used in modeling. This is generally collected from nearby meteorological station but this causes inaccuracy of the results. In this paper, the Weather Research and Forecasting (WRF) model has been used to generate onsite data on nine meteorological parameters. The modeling of six roads of Chembur has been performed using above meteorological data. This approach gives good results of traffic modeling. The results of AERMOD are compared with observed air quality which has contribution from all sources in the region and relative contribution of vehicular sources identified.
Awkash Kumar; Anil Kumar Dikshit; Sadaf Fatima; Rashmi S. Patil. Application of WRF Model for Vehicular Pollution Modelling Using AERMOD. Atmospheric and Climate Sciences 2015, 05, 57 -62.
AMA StyleAwkash Kumar, Anil Kumar Dikshit, Sadaf Fatima, Rashmi S. Patil. Application of WRF Model for Vehicular Pollution Modelling Using AERMOD. Atmospheric and Climate Sciences. 2015; 05 (02):57-62.
Chicago/Turabian StyleAwkash Kumar; Anil Kumar Dikshit; Sadaf Fatima; Rashmi S. Patil. 2015. "Application of WRF Model for Vehicular Pollution Modelling Using AERMOD." Atmospheric and Climate Sciences 05, no. 02: 57-62.
The objective of this research is to develop a tool for planning and managing the water quality of River Godavari. This is achieved by classifying the pollution levels of Godavari River into several categories using water quality index and a clustering approach that ensure simple but accurate information about the pollution levels and water characteristics at any point in Godavari River in Maharashtra. The derived water quality indices and clusters were then visualized by using a Geographical Information System to draw thematic maps of Godavari River, thus making GIS as a decision support system. The obtained maps may assist the decision makers in managing and controlling pollution in the Godavari River. This also provides an effective overview of those spots in the Godavari River where intensified monitoring activities are required. Consequently, the obtained results make a major contribution to the assessment of the State’s water quality monitoring network. Three significant groups (less polluted, moderately and highly polluted sites) were detected by Cluster Analysis method. The results of Discriminant Analysis revealed that five parameters i.e. pH, Dissolved Oxygen (DO), Faecal Coliform (FC), Total Coliform (TC) and Ammonical Nitrogen (NH3-N) were necessary for analysis in spatial variation. Using discriminant function developed in the analysis, 100% of the original sites were correctly classified.
Indrani Gupta; Awkash Kumar; Chandrakant Singh; Rakesh Kumar. Detection and Mapping of Water Quality Variation in the Godavari River Using Water Quality Index, Clustering and GIS Techniques. Journal of Geographic Information System 2015, 07, 71 -84.
AMA StyleIndrani Gupta, Awkash Kumar, Chandrakant Singh, Rakesh Kumar. Detection and Mapping of Water Quality Variation in the Godavari River Using Water Quality Index, Clustering and GIS Techniques. Journal of Geographic Information System. 2015; 07 (02):71-84.
Chicago/Turabian StyleIndrani Gupta; Awkash Kumar; Chandrakant Singh; Rakesh Kumar. 2015. "Detection and Mapping of Water Quality Variation in the Godavari River Using Water Quality Index, Clustering and GIS Techniques." Journal of Geographic Information System 07, no. 02: 71-84.