Understanding carbon sequestration trends using model and satellite data under different ecosystems in India.

This study discusses carbon sequestration variability in different ecosystems of India. Four different biosphere regions, each over 0.5° × 0.

Regional and temporal variability of Indian summer monsoon rainfall in relation to El Niño southern oscillation.

The Indian summer monsoon rainfall (ISMR) exhibits significant variability, affecting the food and water security of the densely populated Indian subcontinent. The two dominant spatial modes of ISMR variability are associated with the El Niño Southern Oscillation (ENSO) and the strength of the semi-permanent monsoon trough along with related variability in monsoon depressions, respectively. Although the robust teleconnection between ENSO and ISMR has been well established for several decades, the major drivers leading to the time-varying relationship between ENSO and ISMR patterns across different regions of the country are not well understood.

Impact of climate extreme events and their causality on maize yield in South Africa.

Extreme climate events can have a significant negative impact on maize productivity, resulting in food scarcity and socioeconomic losses. Thus, quantifying their effect is needed for developing future adaptation and mitigation strategies, especially for countries relying on maize as a staple crop, such as South Africa. While several studies have analyzed the impact of climate extremes on maize yields in South Africa, little is known on the quantitative contribution of combined extreme events to maize yield variability and the causality link of extreme events.

Spatial distributions of PM-bound metal elements in the central part of western Taiwan and their potential emission sources and the carcinogenic health risks.

This study aimed to investigate the spatial distribution of metal elements in PM and their potential sources and associated health risks over a period of two years in eight locations in the central part of western Taiwan. The study revealed that the mass concentration of PM and the total mass concentration of 20 metal elements in PM were 39.0 μg m and 4.

Biomarker responses in the Coilia dussumieri exposed to petroleum hydrocarbons contamination in urbanized estuaries along the west coast of India.

The estuaries of Maharashtra are under continuous pressure due to uncontrolled dumping of persistent toxic organic pollutants into these regions and their impact on the marine environment. In this study, total petroleum hydrocarbons (TPHs) in water, sediments, fish, and biomarker responses in the Coilia dussumieri exposed to TPHs were determined in seven urbanized tropical estuaries along the west coast of India during the winter and summer. The results of the cluster analysis highlighted that the concentration of TPHs in the water, sediment, and fish in the study area was spatially varied, with the highest concentration in the northern area of Maharashtra (NM) estuaries than in the southern region of Maharashtra (SM) estuaries during both seasons.

Use of marine microorganisms in designing anti-infective strategies for sustainable aquaculture production.

Aquaculture, a noteworthy food production sector, is confronted with disease occurrences. Treatment of aquaculture pathogens with antibiotics is often rendered ineffective due to biofilm formation and the development of resistant strains. Marine ecosystems encompass unusual microorganisms that produce novel bioactive compounds, including agents that could be used as alternatives to antibiotics.

Stubble-burning activities in north-western India in 2021: Contribution to air pollution in Delhi.

Stubble-burning in northern India is an important source of atmospheric particulate matter (PM) and trace gases, which significantly impact local and regional climate, in addition to causing severe health risks. Scientific research on assessing the impact of these burnings on the air quality over Delhi is still relatively sparse. The present study analyzes the satellite-retrieved stubble-burning activities in the year 2021, using the MODIS active fire count data for Punjab and Haryana, and assesses the contribution of CO and PM from such biomass-burning activities to the pollution load in Delhi.

Tree-rings stable isotope (δO and δH) based 368 years long term precipitation reconstruction of South Eastern Kashmir Himalaya.

The hydroclimatic variability in Kashmir Himalaya is influenced by the western disturbances and the Indian Summer Monsoon. To investigate long-term hydroclimatic variability, 368 years tree-ring oxygen and hydrogen isotope ratios (δO and δH) extending from 1648 to 2015 CE were analysed. These isotopic ratios are calculated using five core samples of Himalayan silver fir (Abies pindrow) collected from the south-eastern region of Kashmir valley.

DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas.

Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 - 10 km) and neighborhood (order of 0.

Characteristics of elevated aerosol layer over the Indian east coast, Kattankulathur (12.82N, 80.04°E): A northeast monsoon region.

The elevated aerosol layer (EAL) plays a vital role in weather and climate by modifying the Earth's radiation budget. In the present study, the EAL occurrence and its characteristics in the pre-monsoon season using micropulse lidar (MPL) observations during 2016-2018 and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) during 2007-2018 over Kattankulathur is being reported. We have collected 147 days (101 cases) of MPL (CALIPSO) observations during clear sky conditions in the pre-monsoon 2016-2018 (2007-2018), out of which EAL is observed for 56 days (61 cases).

Reassessing the availability of crop residue as a bioenergy resource in India: A field-survey based study.

Second-generation bioenergy, a carbon neutral or negative renewable resource, is crucial to achieving India's net-zero emission targets. Crop residues are being targeted as a bioenergy resource as they are otherwise burned on-field, leading to significant pollutant emissions. But estimating their bioenergy potential is problematic because of broad assumptions about their surplus fractions.

Comparison of ozonesonde measurements in the upper troposphere and lower Stratosphere in Northern India with reanalysis and chemistry-climate-model data.

The variability and trend of ozone (O) in the Upper troposphere and Lower Stratosphere (UTLS) over the Asian region needs to be accurately quantified. Ozone in the UTLS radiatively heats this region and cools the upper parts of the stratosphere. This results in an impact on relative humidity, static stability in the UTLS region and tropical tropopause temperature.

Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India.

The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area.

Sensitivity of WRF/Chem simulated PM2.5 to initial/boundary conditions and planetary boundary layer parameterization schemes over the Indo-Gangetic Plain.

The ability of a chemical transport model to simulate accurate meteorological and chemical processes depends upon the physical parametrizations and quality of meteorological input data such as initial/boundary conditions. In this study, weather research and forecasting model coupled with chemistry (WRF-Chem) is used to test the sensitivity of PM predictions to planetary boundary layer (PBL) parameterization schemes (YSU, MYJ, MYNN, ACM2, and Boulac) and meteorological initial/boundary conditions (FNL, ERA-Interim, GDAS, and NCMRWF) over Indo-Gangetic Plain (Delhi, Punjab, Haryana, Uttar Pradesh, and Rajasthan) during the winter period (December 2017 to January 2018). The aim is to select the model configuration for simulating PM which shows the lowest errors and best agreement with the observed data.

Remote sensing and climate services improve irrigation water management at farm scale in Western-Central India.

The enormous progress in weather and extended range predictions for the Indian monsoon over the last decade has not been translated to operationalized irrigation water management tools despite many agricultural advisories from operational agencies. The limited implementation is mainly due to the resolution mismatches of forecasts and decision-needs and a lack of soil moisture monitoring networks. Sustained soil moisture monitoring suffers from the high cost to farmers in installing distributed sensors.

Machine learning based quantification of VOC contribution in surface ozone prediction.

The prediction of surface ozone is essential attributing to its impact on human and environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone concentration; particularly in urban areas where VOC limited regimes are prominent. The limited measurements of VOCs, however, hinder assessing the VOC-ozone relationship.

Air quality and health co-benefits of climate change mitigation and adaptation actions by 2030: an interdisciplinary modeling study in Ahmedabad, India.

Climate change-driven temperature increases worsen air quality in places where coal combustion powers electricity for air conditioning. Climate solutions that substitute clean and renewable energy in place of polluting coal and promote adaptation to warming through reflective cool roofs can reduce cooling energy demand in buildings, lower power sector carbon emissions, and improve air quality and health. We investigate the air quality and health co-benefits of climate solutions in Ahmedabad, India-a city where air pollution levels exceed national health-based standards-through an interdisciplinary modeling approach.

Impact assessment of surface ozone exposure on crop yields at three tropical stations over India.

Surface ozone is a damaging pollutant for crops and ecosystems, and the ozone-induced crop losses over India remain uncertain and a topic of debate due to a lack of sufficient observations and uncertainties involved in the modeled results. In this study, we have used the observational data from MAPAN (Modelling Air Pollution And Networking) for the first time to estimate the relative yield losses, crop production losses, and economic losses for the two major crops (wheat and rice). The detailed estimation has been done focusing on three individual suburban sites over India (Patiala, Tezpur, and Delhi) and compared with other related studies over the Indian region.

Seasonal variability in size-resolved hygroscopicity of sub-micron aerosols over the Western Ghats, India: Closure and parameterization.

Hygroscopicity of atmospheric aerosol primarily depends on the size and chemical composition of the particle and is important for estimating anthropogenic aerosol radiative forcing. There is limited information exists over the Indian region on size segregated aerosol hygroscopicity (κ) in different seasons. This study presents 'κ' as derived from a Humidified Tandem Differential Mobility Analyzer (HTDMA) over a High Altitude Cloud Physics Laboratory (HACPL) in the Western Ghats, India for more than a year (from May 2019 to May 2020).

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models.

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models.

Decoding the PLFA profiling of microbial community structure in soils contaminated with municipal solid wastes.

This study aimed to assess the influence of municipal solid waste (MSW) disposal on soil microbial communities. Soil samples from 20 different locations of an MSW dumping site contaminated with toxic heavy metals (HMs) and a native forest (as control) were collected for phospholipid fatty acid (PLFA) profiling to predict microbial community responses towards unsegregated disposal of MSW. PLFA biomarkers specific to arbuscular mycorrhizal fungi (AMF), Gram-negative and Gram-positive bacteria, fungi, eukaryotes, actinomycetes, anaerobes, and microbial stress markers-fungi: bacteria (F/B) ratio, Gram-positive/Gram-negative (GP/GN) ratio, Gram-negative stress (GNStr) ratio and predator/prey ratio along with AMF spore density and the total HM content (Cu, Cr, Cd, Mn, Zn, and Ni) were assessed.