In this study we present the seasonal chemical characteristics and potential sources of PM10 at an urban location of Delhi, India during 2010?2019. The concentrations of carbonaceous aerosols [organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC) and water insoluble organic carbon (WIOC)] and elements (Al, Fe, Ti, Cu, Zn, Mn, Pb, Cr, F, Cl, Br, P, S, K, As, Na, Mg, Ca, B, Ni, Mo, V, Sr, Zr and Rb) in PM10 were estimated to explore their possible sources. The annual average concentration (2010–2019) of PM10 was computed as 227?±?97 µg m?3 with a range of 34?734 µg m?3. The total carbonaceous aerosols in PM10 was accounted for 22.5% of PM10 mass concentration, whereas elements contribution to PM10 was estimated to be 17% of PM10. The statistical analysis of OC vs. EC and OC vs. WSOC of PM10 reveals their common sources (biomass burning and/or fossil fuel combustion) during all the seasons. Enrichment factors (EFs) of the elements and the relationship of Al with other crustal metals (Fe, Ca, Mg and Ti) of PM10 indicates the abundance of mineral dust over Delhi. Principal component analysis (PCA) extracted the five major sources [industrial emission (IE), biomass burning?+?fossil fuel combustion (BB?+?FFC), soil dust, vehicular emissions (VE) and sodium and magnesium salts (SMS)] of PM10 in Delhi, India. Back trajectory and cluster analysis of airmass parcel indicate that the pollutants approaching to Delhi are mainly from Pakistan, IGP region, Arabian Sea and Bay of Bengal.
This study presents the chemical composition (carbonaceous and nitrogenous components) of aerosols (PM2.5 and PM10) along with stable isotopic composition (δ13C and δ15N) collected during winter and the summer months of 2015–16 to explore the possible sources of aerosols in megacity Delhi, India. The mean concentrations (mean?±?standard deviation at 1σ) of PM2.5 and PM10 were 223?±?69 µg m?3 and 328?±?65 µg m?3, respectively during winter season whereas the mean concentrations of PM2.5 and PM10 were 147?±?22 µg m?3 and 236?±?61 µg m?3, respectively during summer season. The mean value of δ13C (range: ??26.4 to ??23.4‰) and δ15N (range: 3.3 to 14.4‰) of PM2.5 were ??25.3?±?0.5‰ and 8.9?±?2.1‰, respectively during winter season whereas the mean value of δ13C (range: ??26.7 to ??25.3‰) and δ15N (range: 2.8 to 11.5‰) of PM2.5 were ??26.1?±?0.4‰ and 6.4?±?2.5‰, respectively during the summer season. Comparison of stable C and N isotopic fingerprints of major identical sources suggested that major portion of PM2.5 and PM10 at Delhi were mainly from fossil fuel combustion (FFC), biomass burning (BB) (C-3 and C-4 type vegitation), secondary aerosols (SAs) and road dust (SD). The correlation analysis of δ13C with other C (OC, TC, OC/EC and OC/WSOC) components and δ15N with other N components (TN, NH4+ and NO3?) are also support the source identification of isotopic signatures.
The upper air data collected from the balloon-borne GLASS Sondes launched from the Oceanic Research Vessel (ORV) Sagar Kanya during the Intensive Field Phase of the Indian Ocean experiment (INDOEX, IFP-99;SK-141 Cruise) are utilized forstudying the variability in the mixed-layer heights observed over the western tropical Indian Ocean and central Arabian Sea. During the entire cruise, typical daytime convective mixed-layer heights (roughly corresponding to 1400 LT) obtained from V and q profiles, were observed to be in the range 200–900 m. Shallowmixed -layer heights are observed, in general, over the Inter-Tropical Convergence Zone (ITCZ). Over the central Arabian Sea, vertical profiles of V and q demonstrate a double mixed-layer structure of the marine atmospheric boundary layer (MABL), which gradually disappears close to the Indian coastline. 相似文献
Estimating observation error covariance matrix properly is a key step towards successful seismic history matching. Typically, observation errors of seismic data are spatially correlated; therefore, the observation error covariance matrix is non-diagonal. Estimating such a non-diagonal covariance matrix is the focus of the current study. We decompose the estimation into two steps: (1) estimate observation errors and (2) construct covariance matrix based on the estimated observation errors. Our focus is on step (1), whereas at step (2) we use a procedure similar to that in Aanonsen et al. 2003. In Aanonsen et al. 2003, step (1) is carried out using a local moving average algorithm. By treating seismic data as an image, this algorithm can be interpreted as a discrete convolution between an image and a rectangular window function. Following the perspective of image processing, we consider three types of image denoising methods, namely, local moving average with different window functions (as an extension of the method in Aanonsen et al. 2003), non-local means denoising and wavelet denoising. The performance of these three algorithms is compared using both synthetic and field seismic data. It is found that, in our investigated cases, the wavelet denoising method leads to the best performance in most of the time. 相似文献
In the Himalayan states of India, with increasing population and activities, large areas of forested land are being converted into other land-use features. There is a definite cause and effect relationship between changing practice for development and changes in land use. So, an estimation of land use dynamics and a futuristic trend pattern is essential. A combination of geospatial and statistical techniques were applied to assess the present and future land use/land cover scenario of Gangtok, the subHimalayan capital of Sikkim. Multi-temporal satellite imageries of the Landsat series were used to map the changes in land use of Gangtok from 1990 to 2010. Only three major land use classes (built-up area and bare land, step cultivated area, and forest) were considered as the most dynamic land use practices of Gangtok. The conventional supervised classification, and spectral indices-based thresholding using NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were applied along with the accuracy assessments. Markov modelling was applied for prediction of land use/land cover change and was validated. SAVI provides the most accurate estimate, i.e., the difference between predicted and actual data is minimal. Finally, a combination of Markov modelling and SAVI was used to predict the probable land-use scenario in Gangtok in 2020 AD, which indicted that more forest areas will be converted for step cultivation by the year 2020. 相似文献
The Central Indian Tectonic Zone (CITZ) is a Proterozoic suture along which the Northern and Southern Indian Blocks are inferred to have amalgamated forming the Greater Indian Landmass. In this study, we use the metamorphic and geochronological evolution of the Gangpur Schist Belt (GSB) and neighbouring crustal units to constrain crustal accretion processes associated with the amalgamation of the Northern and Southern Indian Blocks. The GSB sandwiched between the Bonai Granite pluton of the Singhbhum craton and granite gneisses of the Chhotanagpur Gneiss Complex (CGC) links the CITZ and the North Singhbhum Mobile Belt. New zircon age data constrain the emplacement of the Bonai Granite at 3,370 ± 10 Ma, while the magmatic protoliths of the Chhotanagpur gneisses were emplaced at c. 1.65 Ga. The sediments in the southern part of the Gangpur basin were derived from the Singhbhum craton, whereas those in the northern part were derived dominantly from the CGC. Sedimentation is estimated to have taken place between c. 1.65 and c. 1.45 Ga. The Upper Bonai/Darjing Group rocks of the basin underwent major metamorphic episodes at c. 1.56 and c. 1.45 Ga, while the Gangpur Group of rocks were metamorphosed at c. 1.45 and c. 0.97 Ga. Based on thermobarometric studies and zircon–monazite geochronology, we infer that the geological history of the GSB is similar to that of the North Singhbhum Mobile Belt with the Upper Bonai/Darjing and the Gangpur Groups being the westward extensions of the southern and northern domains of the North Singhbhum Mobile Belt respectively. We propose a three‐stage model of crustal accretion across the Singhbhum craton—GSB/North Singhbhum Mobile Belt—CGC contact. The magmatic protoliths of the Chhotanagpur Gneisses were emplaced at c. 1.65 Ga in an arc setting. The earliest accretion event at c. 1.56 Ga involved northward subduction and amalgamation of the Upper Bonai Group with the Singhbhum craton followed by accretion of the Gangpur Group with the Singhbhum craton–Upper Bonai Group composite at c. 1.45 Ga. Finally, continent–continent collision at c. 0.96 Ga led to the accretion of the CGC with the Singhbhum craton–Upper Bonai Group–Gangpur Group crustal units, synchronous with emplacement of pegmatitic granites. The geological events recorded in the GSB and other units of the CITZ only partially overlap with those in the Trans North China Orogen and the Capricorn Orogen of Western Australia, indicating that these suture zones are not correlatable. 相似文献
Theoretical and Applied Climatology - The Dooars region of West Bengal in India is a major tea producing region that contributes around 25% of the national tea yield. Changes in weather patterns... 相似文献
