Decadal growth in emission load of major air pollutants in Delhi

The Indian capital megacity of Delhi is reeling under deteriorating air quality, and control measures are not yielding any significant changes, mainly due to a poor understanding of sources of emissions; hence, priority option in mitigation planning is lacking. In this paper, we have made an attempt to develop a spatially resolved technological high-resolution gridded (∼0.4km×0.4km) emission inventory for eight major pollutants of the Delhi region where high-resolution activity data of all possible major and unattended minor sources are generated by organizing a mega-campaign involving hundreds of volunteers. It is for the first time that we are able to estimate the decadal growth in emissions of various pollutants by comparing newly developed 2020 emissions with SAFAR (System of Air Quality and Weather Forecasting and Research) emissions of 2010 using the identical methodology and quantum of activity data. The estimated annual emissions for PM2.5, PM10, CO, NOx, VOC, SO2, BC, and OC over the Delhi National Capital Region (NCR) are estimated to be 123.8, 243.6, 799.0, 488.9, 730.0, 425.8, 33.6, and 20.3 Gg yr−1, respectively, for the year 2020. The decadal growth (2010–2020) in PM2.5 and PM10 is found to be marginal at 31 % and 3 %, respectively. The maximum growth is found to be in the transport sector followed by the industrial and other sectors. Maximum decadal growth found for the pollutants BC, OC, and NOx is 57 %, 34 %, and 91 %, respectively. The decadal shift of sectorial emissions with changing policies is examined. The complete dataset is available on Zenodo at https://doi.org/10.5281/zenodo.7715595 (Sahu et al., 2023).</p

Notes for genera: basal clades of Fungi (including Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota)

Compared to the higher fungi (Dikarya), taxonomic and evolutionary studies on the basal clades of fungi are fewer in number. Thus, the generic boundaries and higher ranks in the basal clades of fungi are poorly known. Recent DNA based taxonomic studies have provided reliable and accurate information. It is therefore necessary to compile all available information since basal clades genera lack updated checklists or outlines. Recently, Tedersoo et al. (MycoKeys 13:1--20, 2016) accepted Aphelidiomycota and Rozellomycota in Fungal clade. Thus, we regard both these phyla as members in Kingdom Fungi. We accept 16 phyla in basal clades viz. Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota. Thus, 611 genera in 153 families, 43 orders and 18 classes are provided with details of classification, synonyms, life modes, distribution, recent literature and genomic data. Moreover, Catenariaceae Couch is proposed to be conserved, Cladochytriales Mozl.-Standr. is emended and the family Nephridiophagaceae is introduced

Not Available

Not AvailableNot AvailableNot Availabl

Study of Advanced Techniques to Predict the Soil Properties

Information about soil properties helps the farmers to adopt effective and efficient farming practices, which can increase higher yields with optimum usage of farm resources. An attempt has been made in this paper to predict soil properties using geospatial kriging approaches. This study mainly focuses on predicting soil pH using different kriging methods. Soil pH dramatically affects many other soil processes, such as nitrification and denitrification, mineralization, precipitation, and dissolution of soil organic matter. Total of seven kriging semivariogram models, namely spherical, circular, exponential, Gaussian, and linear, while two models of universal kriging, such as linear with linear drift and linear with quadratic drift, have been taken to interpolate the spatial soil pH. The performances of these entire models have been validated using mean error, and root mean square error. Spatial analysis revealed that Universal kriging outperformed ordinary kriging with less mean error and root mean square error, 0.016 and 0.52, respectively. The spatial analysis of soil mapping can be instrumental in adopting real-time and on-the-go soil precision practices

Assessment of Stress Tolerant Rice Varieties under Rain Fed Condition in North Eastern Ghat of Odisha

A field experiment was conducted during kharif 2019 at NICRA villages viz. Nada, Chikili and Chopara of Krishi Vigyan Kendra, Ganjam1 through technology demonstration to analyse the performance of stress tolerant rice varieties under rainfed condition. Three different drought tolerant rice varieties i.e. MTU-1010, Sahabhagidhan and Swarna Shreya with ten replications were taken in random block design with improved package of practices compared to farmers’ practice. The results of technology demonstration revealed that growing of Swarna Shreya recorded higher growth parameters compared to other treatments with ten days delayed maturity. Improved practice of Swarna Shreya (T3) recorded higher number of filled grains panicle-1 (135.4±5.41), spikelet fertility (95.0±0.33 %), panicle length (24.8±1.45 cm) and 1000 grain weight (25.7±0.52 g) compared to other two treatments. Significantly higher grain yield (3588±169.5 kg &nbsp;&nbsp;ha-1) and straw yield (7591±236.9 kg ha-1) were recorded compared to farmers’ practice (2632±125.1 kg ha-1 and 5934±366.4 kg ha-1, respectively). Swarna Shreya recorded higher gross return (Rs 67037±2980.2 ha-1), net return (Rs 37037±2980.2 ha-1), B: C ratio (2.23±0.20) and profitability (Rs. 101.5±8.16 ha-1 days) with as compared to farmers’ practice of growing MTU 1010