Assessment of PM<Subscript>2.5</Subscript> chemical compositions in Delhi: primary vs secondary emissions and contribution to light extinction coefficient and visibility degradation

Haze-fog conditions over northern India are associated with visibility degradation and severe attenuation of solar radiation by airborne particles with various chemical compositions. PM_2.5 samples have been collected in Delhi, India from December 2011 to November 2012 and analyzed for carbonaceous and inorganic species. PM₁₀ measurements were made simultaneously such that PM_10–2.5 could be estimated by difference. This study analyzes the temporal variation of PM_2.5 and carbonaceous particles (CP), focusing on identification of the primary and secondary aerosol emissions, estimations of light extinction coefficient (b_ext) and the contributions by the major PM_2.5 chemical components. The annual mean concentrations of PM_2.5, organic carbon (OC), elemental carbon (EC) and PM_10–2.5 were found to be 153.6 ± 59.8, 33.5 ± 15.9, 6.9 ± 3.9 and 91.1 ± 99.9 μg m^?3, respectively. Total CP, secondary organic aerosols and major anions (e.g., SO₄ ^2? and NO₃ ^?) maximize during the post-monsoon and winter due to fossil fuel combustion and biomass burning. PM_10–2.5 is more abundant during the pre-monsoon and post-monsoon. The OC/EC varies from 2.45 to 9.26 (mean of 5.18 ± 1.47), indicating the influence of multiple combustion sources. The b_ext exhibits highest values (910 ± 280 and 1221 ± 371 Mm^?1) in post-monsoon and winter and lowest in monsoon (363 ± 110 and 457 ± 133 Mm^?1) as estimated via the original and revised IMPROVE algorithms, respectively. Organic matter (OM =1.6 × OC) accounts for ~39 % and ~48 % of the b_ext, followed by (NH₄)₂SO₄ (~21 % and ~24 %) and EC (~13 % and ~10 %), according to the original and revised algorithms, respectively. The b_ext estimates via the two IMPROVE versions are highly correlated (R² = 0.95, root mean square error = 38 % and mean bias error = 28 %) and are strongly related to visibility impairment (r = ?0.72), mostly associated with anthropogenic rather than natural PM contributions. Therefore, reduction of CP and precursor gas emissions represents an urgent opportunity for air quality improvement across Delhi.     

Analysis and very short range forecast of cyclone “AILA” with radar data assimilation with rapid intermittent cycle using ARPS 3DVAR and cloud analysis techniques

In this study, both reflectivity and radial velocity are assimilated into the Weather Research and Forecasting (WRF) model using ARPS 3DVAR technique and cloud analysis procedure for analysis and very short range forecast of cyclone ÁILA. Doppler weather radar (DWR) data from Kolkata radar are assimilated for numerical simulation of landfalling tropical cyclone. Results show that the structure of cyclone AILA has significantly improved when radar data is assimilated. Radar reflectivity data assimilation has strong influence on hydrometeor structures of the initial vortex and precipitation pattern and relatively less influence is observed on the wind fields. Divergence/convergence conditions over cyclone inner-core area in the low-to-middle troposphere (600–900 hPa) are significantly improved when wind data are assimilated. However, less impact is observed on the moisture field. Analysed minimum sea level pressure (SLP) is improved significantly when both reflectivity and wind data assimilated simultaneously (RAD-ZVr experiment), using ARPS 3DVAR technique. In this experiment, the centre of cyclone is relocated very close to the observed position and the system maintains its intensity for longer duration. As compared to other experiments track errors are much reduced and predicted track is very much closer to the best track in RAD-ZVr experiment. Rainfall pattern and amount of rainfall are better captured in this experiment. The study also reveals that cyclone structure, intensification, direction of movement, speed and location of cyclone are significantly improved and different stages of system are best captured when both radar reflectivity and wind data are assimilated using ARPS 3DVAR technique and cloud analysis procedure. Thus optimal impact of radar data is realized in RAD-ZVr experiment. The impact of DWR data reduces after 12 h forecast and it is due to the dominance of the flow from large-scale global forecast system model. Successful coupling of data assimilation package ARPS 3DVAR with WRF model for Indian DWR data is also demonstrated.     

Microstructures and compositional variation in the intravolcanic bole clays from the eastern Deccan volcanic province: Palaeoenvironmental implications and duration of volcanism

Clay minerals associated with intra-volcanic bole horizons of varied colours and thicknesses contain montmorillonite, halloysite and kaolinite, show distinct microstructures and microaggregates. In kaolinite, Fe³⁺ ions substitute for Al³⁺ at octahedral sites. Most of these clays are dioctahedral type, show balance between net layer and interlayer charges. The interstratified illite — smectite (I/S) mixed layers containing variable proportions of montmorillonite. Illite contains sheet-like, well oriented microaggregates. The parallel stacks of chlorite sheets show chlorite/smectite (C/S) mixed layers. Progressive enrichment of Fe and depletion of Al ions with the advancement of kaolinization process is observed. High order of structural and compositional maturity observed in these bole clays, indicate long hiatus between the two volcanic episodes.