Meteorology plays a crucial role in air quality. The presence of uncertainties of a significant nature in the meteorological profile used during air quality model simulation has the potential to affect negatively the results of the simulations. This paper describes a most recent version of the meteorological model called Weather Research and Forecasting (WRF) model and its importance in air quality. The performance of WRF depends upon the intended application and parameterization scheme of physics options. WRF model is also applied to investigate the simulation results with various land surface models (LSMs) and Planetary Boundary Layer (PBL) parameterizations and various set of microphysics options. It predicts various meteorological spatial parameters like mixing layer height, temperature, humidity, rain fall, cloud cover and wind. The WRF results are integrated with air quality model (AQM) and the AQM depends upon the performance of WRF. It has been applied for evaluation of national pollution control policy, behaviour of plume rise, property of aerosols, prediction of Ozone, SO2, NOx, PM10, PM2.5 etc. using AQM for various sources. The effect of topography and different seasons on the concentration of pollutants in the atmosphere has also been studied using AQM. AQM AERMOD has also been reviewed with various other AQM models such as ADMS-Urban and CALPUFF. AERMOD has been used for different time scales, health risk assessment, evaluation of various control strategies, Environmental Impact Assessment (EIA) studies and emission factor estimation. This paper presents the importance of meteorological model to AQM as well as many applications of AQM to demonstrate various scientific questions and policies.

Air pollution is one of the most critical concerns encountered in urban areas, especially in developing nations. Four out of five cities in the world with the worst air quality are located in India. The concentration levels of air pollutants, including sulfur dioxide (SO2), nitrogen dioxide (NO2), and suspended particulate matter (SPM), are monitored by instituted air monitoring stations. The present study assesses the spatial air quality and the fitness of spatial mapping using data collected by monitoring stations in Mumbai, India. As part of this, air quality was monitored at twelve selected locations in Mumbai city. The collected data were spatially interpolated using the available interpolation tools of ArcGIS, including inverse distance weight (IDW), kriging (spherical and Gaussian), and spline techniques by the leave-one-out scheme. In each case, the interpolated concentrations of SO2, NO2, and SPM at the eleven locations were compared to the observed values of the unmonitored locations in the corresponding region. Percentage error, normal mean bias (NMB), normal root mean square error (NRMSE), and degree of agreement (“d”) were carried out to evaluate the spatial interpolation methods adopted for air quality mapping. The spline method is not preferred for spatial mapping due to negative concentrations in many locations due to either overestimation or underestimation. The minimum average percentage error for SO2 and NO2 mapping is achieved using IDW interpolation, while SPM mapping is achieved by the kriging method. The NMB and “d” were minimum for IDW, while NRMSE was minimum for the kriging Gaussian method in all the cases. The results of this study can be used in low-middle-income countries (LMIC) to obtain an aerial view of air quality to help the regulatory bodies in policy formulation and decision-making. This method needs fewer resources and time when compared to setting up new monitoring stations or using dispersion modeling to obtain an aerial perspective.

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.

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.

India experiences different types of seasons each year due to its position on the globe. Seasonal and spatial scale-based variation in weather conditions and topographical features of the region result in various climate of that specific region. Measured weather parameters are analyzed and variations can be seen with respect to the desired time resolution. However, measured parameters represents micro-meteorology and space-based extrapolation is difficult in the manners of topography, land use and its physics. Simulation-based mesoscale weather parameters can be analyzed and variation for short period can be seen. Ahmedabad city of Gujarat State in India has been taken as a study area for analysis of meteorological parameters. Weather parameters were obtained by simulating weather research and forecasting model using two way nested for three domains and evaluated with micrometeorological tower observations in the previous study. Various weather parameters such as temperature, rainfall, humidity, wind speed, wind direction and cloud cover were studied with respect to 3 hourly intervals for 3 months (January, February and March) for the year of 2015. A statistical analysis including average, correlation and standard deviation was carried out for the parameters to study the weather for the city. It was found that the all parameters have maximum deviation from 12 noon to 18 p.m. except wind in a day over the period. More variations were seen between February 27, 2015 and March 02, 2015 for all the parameters, which is majorly due to the season change from the retreating winter to the onset of summer. This study can help to understand the change in micro and mesoscale meteorology based on atmospheric earth system.

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.

Generally, ambient Ammonia (NH 3 ) concentration level is always under prescribed limit of government regulatory authorities but the concentration level tends to be higher in surrounding regions of a chemical fertilizer industry. There are many chemical fertilizer industries across the world and 9 public and 18 private fertilizers industries in India. Mostly, air quality monitoring is carried out for many gaseous pollutants and dust such as SO 2 , NO 2 , SPM, PM 10 and PM 2.5 but NH 3 is monitored at only few selected locations. Maravali region of Mumbai city has a public sector fertilizer company and this region has maximum concentration of NH 3 in Mumbai city. In this study, the spatial average concentration of NH 3 was estimated for Mumbai city including and excluding the air quality monitoring site of Maravali, where fertilizer industry is present. The spatial average concentration of Mumbai city is 85 µg/m 3 and 56 µg/m 3 including and excluding Maravali respectively. The maximum concentration of NH 3 is at Maravali and annual average concentration here is 342 µg/m 3 . This is 6.1 times more of spatial average concentration of Mumbai excluding Maravali. The same was visualized and represented in spatial concentration mapping using Inverse Distance Weighting (IDW) interpolation technique of ArcGIS tool. Also, health impact assessment was carried out for Mumbai city due to the concentration level of NH 3 . Local Concentration–Response (C-R) coefficient for Mumbai was used to assess health impact for ammonia. 3.4 and 6.8 Million people were exposed by phlegm and other chest illness respectively in Mumbai city. The economic cost of the health was also estimated for the phlegm due to ammonia which was 57 Million USD(3.9 Billion INR) for the year 2012 for Mumbai city.

Due to increasing population and rising income level, most of the metropolitan cities in the world are facing problems of congestion and pollution.It is high time proper steps were taken to prevent the unbearable congestion and pollution that might occur in the near future.Before taking the mitigation measures, it is important to find out or estimate the level of congestion and pollution.Hence, the study has been conducted to assess the present and future pollution and congestion level for a highly congested Worli Road Network, Mumbai.Level of service analysis has been done for all the roads in the network to find out the congestion level.The impact of the traffic was quantified in terms of Volume/Capacity (V/C) ratio, for the coming 20 years in 10-year intervals.The years in which any road was reaching its theoretical capacity was also identified.USEPA AERMOD has been used with proper evaluation of the model results to model the concentration of air pollutants for present and future scenario.Through the predicted results of congestion and air pollution in future, some mitigation measures are suggested.

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.

Pressure on infrastructure due to over population has deteriorated the indoor environment causing various health issues. It has also contributed to the sick building syndrome making huge monetary burden to economy. Public health department of the country has taken many actions to mitigate these issues however; design of the building was not taken into consideration. Optimum quantities of light and proper ventilation express the quality of indoor environment. Also, the use of natural light and ventilation is definitely an advantage with the raising concerns regarding the cost and environmental impact of energy use. Natural light and ventilation can reduce building construction and operation costs and reduce the energy consumption. Moreover it would also ensure safe, healthy and comfortable living conditions. Therefore, it is very important to assess indoor environment before implementing new construction or building. This provides theoretical guidelines and basic calculations for understanding a green infrastructures and the factors related to it. In this paper, a building has been studied in an urban city of India where the percentage area of light and ventilation were analyzed Analysis showed the percentage of light is thrice and ventilation is twice the prescribed limits by Indian Green Building Council (IGBC). It has been found that building under study fulfills the given criteria by IGBC. This analysis can be useful while constructing a new infrastructure to improve the standard of living as 90% time is spent indoors.

Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRF model and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the “air pollution control region” by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF–AERMOD system was applied for prediction of concentration levels of pollutants SO 2 , NO x and PM 10 . A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers.

Air pollution is one of the major threats to environment in the present time. Increase in degree of urbanization is a major cause of this air pollution. Due to urbanization, vehicular activities are continuously increasing at a tremendous rate. Mobile or vehicular pollution is predominantly degrading the air quality worldwide. Thus, air quality management is necessary for dealing with this severe problem. The first step to deal with this air pollution problem is to find out the existing concentration of air pollutants in the atmosphere due to vehicular activities. It is not possible to establish ambient air monitoring stations everywhere, especially in developing countries as it is a costly process. Hence, vehicular air quality models are used to predict the concentration of different pollutants in the atmosphere. This review covers the simulation of vehicular emission by different types of models for estimating the pollutant concentration in ambient air from vehicular emissions. The models predict concentrations of pollutants in time and space and relate it to the dependent variables. These can also be used to predict the concentration of pollutants in the future. These models can be useful for imposing regulations by governments and to test techniques for controlling pollutant emissions. This review also discusses where and how the respective models can be used.

Weather plays an important role in all phenomena such as air pollution and daily life. It has been found that weather and climate affect human health and well-being. A substantial mortality has been seen in the winter as well as summer season. Many cities have determined minimum and maximum temperature which is associated with total mortality in the city. Humidity affects the body's ability to cool itself by evaporation of perspiration. In the present study, four weather parameters viz. temperature, wind speed, wind direction and humidity have been studied with respect to three hourly intervals over the year 2012 for Mumbai city. Generally, weather parameters are analysed for continuous time series data for the period but in this case study, each particular eight intervals of three hours a day (8 intervals × 3 hours = 24 hours) for the year 2012 have been analysed. It helps to investigate the average pattern and variation of weather parameters over the year which influences the air pollution and other climatic impacts for each three hourly time interval. The statistical study has been done for this data set which includes minimum, maximum, average and standard deviation of each parameter for the year. Results show that temperatures of night time (23.30 to 8.30) have low variation than day time (11.30 to 20.30). Humidity takes large variation from day to night time, has higher variation from 11.30 to 20.30 than other times. The topographic region of Mumbai causes low wind speed as usual and many times calm is recorded when it does not give any wind data such as wind speed and direction. The variation in wind speed for particular time is marginal but maximum is found between 11.30 to 17.30. Wind is blowing from North, South, North-West and South-West direction. This analysis can help to investigate worst case scenario based on weather parameters which causes poor air quality and several health effects.

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. NO x 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 NO x 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.

The accuracy of the results from an air quality model is governed by the quality of emission and meteorological data inputs in most of the cases. In the present study, two air quality models were applied for inverse modelling to determine the particulate matter emission strengths of urban and regional sources in and around Mumbai in India. The study takes outset in an existing emission inventory for Total Suspended Particulate Matter (TSPM). Since it is known that the available TSPM inventory is uncertain and incomplete, this study will aim for qualifying this inventory through an inverse modelling exercise. For use as input to the air quality models in this study, onsite meteorological data has been generated using the Weather Research Forecasting (WRF) model. The regional background concentration from regional sources is transported in the atmosphere from outside of the study domain. The regional background concentrations of particulate matter were obtained from model calculations with the Danish Eulerian Hemisphere Model (DEHM) for regional sources. The regional background concentrations obtained from DEHM were then used as boundary concentrations in AERMOD calculations of the contribution from local urban sources. The results from the AERMOD calculations were subsequently compared with observed concentrations and emission correction factors obtained by best fit of the model results to the observed concentrations. The study showed that emissions had to be up-scaled by between 14 and 55% in order to fit the observed concentrations; this is of course when assuming that the DEHM model describes the background concentration level of the right magnitude.

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.

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.

Air pollution is increasing rapidly in almost all cities around the world due to increase in population. Mumbai city in India is one of the mega cities where air quality is deteriorating at a very rapid rate. Air quality monitoring stations have been installed in the city to regulate air pollution control strategies to reduce the air pollution level. In this paper, air quality assessment has been carried out over the sample region using interpolation techniques. The technique Inverse Distance Weighting (IDW) of Geographical Information System (GIS) has been used to perform interpolation with the help of concentration data on air quality at three locations of Mumbai for the year 2008. The classification was done for the spatial and temporal variation in air quality levels for Mumbai region. The seasonal and annual variations of air quality levels for SO2, NOx and SPM (Suspended Particulate Matter) have been focused in this study. Results show that SPM concentration always exceeded the permissible limit of National Ambient Air Quality Standard. Also, seasonal trends of pollutant SPM was low in monsoon due rain fall. The finding of this study will help to formulate control strategies for rational management of air pollution and can be used for many other regions.

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 Geo- graphical Information System to draw thematic maps of Godavari River, thus making GIS as a deci- sion support system. The obtained maps may assist the decision makers in managing and control- ling 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 net- work. 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.

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.