The advent of advanced features of soft computing can be used to solve complex problems which are more non-linear and messy. Many of the applications have been analysed and validated by the researchers through soft computing approach in the past.Neural Networks (NN) with appropriate selection of training parameters is implemented apart from conventional mathematical model. In this paper, analysis is made on the estimation of PM10 air quality in selected regions of Chennai district by wavelet approach with energy spectrograms. After analysing the results, NN of multilayer feed forward back propagation algorithm forecasts the air quality of selected regions. Discrepancies in selecting the training parameters of NN’s have been overcome by trial and error basis. This work will be helpful in proving the powerful tool of NN to forecast short term nonlinear parameters and the predicted results will give us the clear design of existing problem and thecontrol measures need to be implemented.

Today, indoor air pollution is a major concern. So far, many quantitative and qualitative studies have been conducted on particulate matter pollution in closed environments, but not much research has been done to measure air pollution in subway station. In this study, we have investigated the concentrations of PM10, PM2.5 and TSP particles in 12 underground stations on the oldest and main Tehran metro line, in two seasons, autumn and spring. For sampling suspended particles, we have used a portable direct reading device for monitoring suspended-particles (HAZDUST EPMA5000). We also used Pair T- test to compare the particle concentrations in different modes of the ventilation system (on, off, and inlet air) and Three-way variance analyze. According to the results, the mean concentrations of PM2.5-PM10 - TSP values in line-1 on the station platforms are significantly higher in spring than in autumn, off state of the ventilation system than on state of the ventilation system (P <0.001). Also, the concentration of particles measured in the air of subway stations is higher in the off state of ventilation systems, compared to Inlet air to stations (P<0.001). There is a correlation between concentration of particles measured in different sampling season, condition of the ventilation mode (on, off, inlet air) (P<0.001). Improving the efficiency of ventilation systems (equipped with a suitable filter) and fan in stations is suggested as one of the factors to reduce the concentration of particles, especially in spring.

Exposure to airborne particulates is a major occupational hazard especially for outdoor workers who spending time outdoors at ground level getting exposed to traffic fumes and roadside dust. Aim of this study was to assess respiratory health symptoms and determine the change of lung functions of the roadside vendors and its association with traffic-related exposures and their working experience. A cross-sectional study was conducted in key market places of Kolkata – Gariahat (GH), Esplanade-Park Street (EP), Shyambazar-Hatibagan (SH) and Behala (BE). Particulate (PM10 and PM2.5) levels and meteorological parameters (wind speed, temperature and relative humidity) were monitored in the morning, afternoon and night over the period of October 2019 to February 2020. Lung function status (FEV1, FVC, FEV1/FVC ratio and PEF) was measured for 111 purposively selected participants. PM concentration was observed higher in the morning and night peak hours for all sites. At SH area the average occupational exposure level for PM10 and PM2.5 were observed as 1502.22 μg/m3h and 684.01 μg/m3h. Percentage predicted FEV1 (%FEV1) of street vendors was found decreasing with their work experience and the worst-case scenario was observed in the EP area, with the corresponding value being 70.75%, 49.15% and 47.3% for less than 10 years, 10 to 20 years and more than 20 years participation respectively. The higher particulate burden was observed to have declining lung function status of the street vendors. A strong policy framework should be adopted to improve outdoor working environment for outdoor workers.

Ozone(O3), and its precursors, Benzene (C6H6), Nitrogen Dioxide(NO2), Carbon Monoxide (CO) and meteorological parameters Temperature, Relative Humidity and Wind Speed were measured in urban air of two sites of significant spatial variations, Delhi Milk Scheme (DMS), Sadipur and Netaji Subhash Chander Institute of Technology(NSIT) Dwarka, during 2017–2018. Samples collected by Central Pollution Control Board (CPCB) has been analysed. The concentrations of Benzene, Nitrogen dioxide and Carbon monoxide were found to be more at DMS than NSIT site in winter season (11.137±3.258, 5.540±1.441, 55.333±12.741, 44.667±10.066μg/m3, 1.433±0.058, 1.033±0.287mg/m3 respectively) and summer season (3.167±1.222, 2.233±0.929, 50.333±2.082, 31.333±6.658μg/m3, 0.743±0.151, 0.443±0.051mg/m3 respectively) while Ozone was found to be more at NSIT than DMS site (40.333±3.215, 34.433±2.503μg/m3 respectively). The maximum concentrations of Benzene for the DMS and NSIT sites, respectively, were 32.4μg/m3 and 17.7μg/m3 and was observed in the month of November while minimum were 1.0μg/m3 and 0.6μg/m3 and was observed in the month of June. For Ozone, the maximum concentrations for the DMS and NSIT sites, respectively, were 100μg/m3 and 101μg/m3 and was observed in the month of June while minimum were 33.0μg/m3 and 28.0μg/m3 and was observed in the month of February and December respectively. Regression analyses were performed to correlate O3 concentrations with C6H6, NO2 and CO in order to infer their possible sources. The study reveals that there is significant correlation of O3 with C6H6 (r2=0.475) and CO (r2=0.985) in summer at DMS and with C6H6 (r2=0.902) &amp; NO2(r2=0.728) in winter at NSIT. The correlation of O3, C6H6, NO2 and CO with Temperature, Relative Humidity and Wind Speed has also been investigated to understand their influence on these pollutants.