Kirti Soni

Variation in aerosol black carbon concentration and its emission estimates at the mega-city Delhi

Simultaneous measurements of aerosol black carbon (BC) mass concentration using an Aethalometer Model AE-42 and mixing layer height (MLH) using a monostatic sonic detection and ranging (SODAR) system were carried out from January 2006 to January 2007 at the mega-city Delhi. The BC concentration generally had a typical diurnal variation with morning and late-afternoon/night peaks. The average BC concentration during the whole period of observation was fairly high at 14.75 μg m−3. The BC concentration nearly doubled during cloudy-sky conditions compared to that during clear-sky conditions. The seasonal variation showed a maximum average concentration during the winter (25.5 μg m−3) and a minimum during the monsoon season (7.7 μg m−3), with post- and pre-monsoon values at 13.7 and 9.4 μg m−3, respectively. The average BC concentrations were strongly affected by the ventilation coefficient, a product of average wind speed (WS) and average MLH, and were found to be strongly anticorrelated. A simple model of BC concentration along with the MLH and WS was applied to estimate the average BC emission, which was found to vary in the range 11 000–17 000 kg of BC per day. The maximum emission during the day averaged every hour for different months lay in the range 1000–2100 kg h−1. The mean monthly emission varied in the range 0.35–0.52 Gg per month, giving rise to an annual estimated emission of 4.86 Gg in the year 2006 over Delhi.

Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models

This study investigates the applicability of three different soft computing methods, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS), and M5 Model Tree (M5-Tree), in forecasting SO2 concentration. These models were applied to monthly data obtained from Janakpuri, Nizamuddin, and Shahzadabad, located in Delhi, India. The models were compared with each other using the cross validation method with respect to root mean square error, mean absolute error, and correlation coefficient. According to the comparison, LSSVR provided better accuracy than the other models, while the MARS model was found to be the second best model in forecasting monthly SO2 concentration. Results indicated that the applied models gave better forecasting accuracy in Janakpuri station than the other stations. The results were also compared with previous studies and satisfactory results were obtained from three methods in modeling SO2 concentrations.

Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling

A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend.

SODAR pattern classification and its dependence on meteorological parameters over a semiarid region of India

ABSTRACT The variability of the atmospheric boundary layer together with meteorological parameters has been investigated over the semi-arid region Delhi. Two sources of the dataset have been used: sound detection and ranging (SODAR) and automatic weather station during the period from December 2013 to November 2014. A Laboratory Virtual Instrument Engineering Workbench (LabVIEW)-based programme has been developed to plot the stability class from A to F directly from the mixing height dataset. Based on the SODAR echograms and mixing height, temporal and seasonal variability of stability classes has been estimated. It is observed that the convective boundary layer height advances and decreases during the daytime depending on the increase and decrease of surface temperature due to solar heating of the ground. From seasonal classification of the stability class, it is observed that the class A and class E are dominated in convection and nocturnal periods in all seasons, whereas class F is not found during the winter and pre-monsoon seasons. Impact of meteorological parameters, that is, wind speed, temperature, and relative humidity on mixing height during different seasons has also been studied.