The article presents an experimental study, which has been carried out in an environmental wind tunnel to measure the mean pressures and pressure differences at inlet and outlet of various openings of a naturally ventilated classroom model of a school building located near an urban roadway in the city of Delhi. The wind pressure coefficients have been measured and analyzed for different wind incidence angles and for varying classroom opening configurations representing the change in behavior of building occupants in different seasons of the year. The article also presents the results of a CFD modeling carried out to estimate the wind velocity and turbulent intensity in and around the classroom model to understand the actual airflow pattern inside the classroom. The present study is an effort to understand the occurrence of ventilation in naturally ventilated buildings and finally to evaluate the indoor air quality in such buildings

Particles were collected in diluted diesel exhaust under high-idle and high-torque operating conditions and from the air above roadsides. We analyzed particle morphology of particles that were 30, 50, 70, and 100 nm in electrical mobility diameter (Dm) by transmission electron microscope. We classified ten types of particles based on particle morphology and found that diesel exhaust particles (DEPs) of Dm=70 and 100 nm composed at least 14% and 58%, respectively, of atmospheric particles at each particle size. From the two-dimensional transmission electron microscope images, we derived three-dimensional morphological properties of the agglomerates and found that the numbers and diameters of the primary particles in the agglomerates were similar to those of DEPs at a given Dm. However, the active surface areas of the roadside atmospheric particles were systematically smaller than those of DEPs.

Wind flow conditions play an important role in the assimilative capacities of urban airsheds. It is desired that urban planners associate due importance to wind flow conditions while designing the cities considering its likely impact on ambient air quality. Further, considering the explosive growth of megacities across the world it is required to rank these cities as per their assimilative capacities where wind flow conditions can play an important role. This study scrutinizes wind flow conditions (stagnation, ventilation and recirculation), associated air quality and emissions for two megacities namely, Delhi and Mumbai to illustrate the pivotal role, the meteorology could play in location of urban airsheds. The study shows a dominance of stagnation conditions with few cases of recirculation events over Delhi. Ambient levels of pollutants were found to correlate positively with stagnation conditions in Delhi. Mumbai was found to have higher ventilation and recirculation events and lower ambient levels of the pollutants. Further, this study for two megacities demonstrated that persistently poor air flow conditions for an urban airshed could lead to poorer air quality even with lower emissions (Delhi) in comparison to a city with lower pollution potential and higher emissions (Mumbai). Simple methodologies as adopted here could be practiced to scrutinise carrying capacities of the cities and to rank these for their pollution potential that could be helpful to regulators for emission control strategies.

In the present work, the Operational Street Pollution Model (OSPM) has been evaluated in comparison with continuous half-hourly measurements over a multi-year period for five permanent street monitor stations that constitute part of the Danish Air Quality Monitoring Programme as well as with passive measurements with long averaging times at nine locations in Copenhagen as part of a specific project. Results are discussed in relation to the quality objective within the EU Air Quality Directive and general uncertainties in model parameters and model input data. It is demonstrated that OSPM reproduces the observed basic dependencies of concentrations on meteorological parameters–most notably wind direction and wind speed. However, in some cases the modelled annual trends in NOx and NO2 are slightly different from what is found in the measured concentrations. For NOx the OSPM reproduces the observations well, especially for the most recent years, while for NO2 the model over-predicts in two cases. The explanation for this over-prediction is believed to be uncertainties in the traffic or emission input data, but also in model parameters, and the representativeness of the urban background data may play an important role. The newly developed evaluation tool is used for exploratory data analysis of the large amount of data, and is free available for the research community. The evaluation tool is complementary to the ‘Delta Tool’ that has been developed in the framework of FAIRMODE by JRC Ispra. OSPM calculations for nine streets with passive sampler measurements were conducted as ‘blind test’ i.e. without knowing the measured values. OSPM calculations were in good agreement with the measurements for seven out of nine street sections. Refinements of the input data lead to a significant improvement of the agreement between model results and measurements at the two remaining locations. Recommendations could be derived for an improved quality assurance of the input data and for minor adjustments in the OSPM.

CALINE4 line source dispersion tool has been applied to model NOx in the study with the help of CALROADS View software. The model was used with and without canyon options activated near Jadavpur University, Kolkata. It is observed to exhibits better correlation for ‘with canyon’ option than ‘without canyon’ against actually measured concentrations of NOx, which is indeed more realistic to reflect actually prevailing condition, as the study site is situated in a street canyon. A calibration equation is also deduced to calculate the corresponding actual i.e. prevailing concentrations form model predicted values for NOx. A typical NOx concentration contour due to traffic is generated around Jadavpur University, Kolkata.

Ozone, which is a secondary pollutant in the troposphere, is very injurious to human health causing irritation of respiratory system, reducing lung capacity, etc. Adversely affecting the plant growth and deteriorating the materials. Thus it is of prime importance to predict the ozone concentration so that effective mitigation strategies can be adopted. As the formation of the tropospheric ozone is dependent on various meteorological parameters and the concentration of various other air pollutants, it is necessary to consider this dependence aspect in the modelling approach. In this background, the present paper puts forward the study of short-term prediction of tropospheric ozone concentration using Artificial Neural Network (ANN) modelling approach for a busy traffic junction of Madras city, one of the four megacities of India. 8-hourly averaged values of 11 air pollutants concentrations and 6 meteorological parameters were used for the study. The respective data was collected at a busy traffic junction of the city for a period of 19 months i.e. during September 2008–March 2010. 70% of the data was used for training the ANN models while the remaining of 30% data was used for validating them. By changing the neural architecture, 34 ANN models were formulated which were statistically analyzed. Based on the encouraging results (d=0.80, r=0.69, etc.), the paper puts forward the suitability of ANN modelling approach for the short-term prediction of tropospheric ozone concentration.