Latest Research

Assessing the Influence of Land Use and Land Cover Data on Cyclonic Winds and Coastal Inundation due to Tropical Cyclones: A Case Study for the East Coast of India

A standalone Advanced Circulation (ADCIRC) model for the east coast of India with a high-resolution grid of 100m near the coast is used to evaluate coastal inundation resulting from the storm tides generated by recent cyclones. A directional surface roughness parametrization that alters wind speed and Manning's n friction coefficient to compute bottom friction based on land use/land cover (LULC) at a particular location is incorporated in the model. ERA5 reanalysis winds highlight that the cyclonic winds over the land are less intensive than nearby coastal oceans, particularly during landfall time, by approximately 29% - 50%. Experiments are also designed to quantify the impact of surface and bottom friction on wind speed and the inward propagation of storm tides. A comparison of cyclonic wind speed after incorporating LULC data in the model is made with an automatic surface observation system (ASOS). It suggests a 15–29% reduction, consistent with ASOS. The inundated area computed for the cyclones advocates a significant reduction (15–50%) due to LULC. Sensitivity experiments with LULC are performed to examine the impact of mangroves in the Krishna estuary located in between two concave-shaped coastal geometries. Replacing wetlands with Mangroves results in a simultaneous decline in wind speed (12.5%) and inundated area (13.4%). It also highlights that bottom friction contributes (9.4%) in the inundated area against surface friction (4%). This study infers that further investigation and planning are mandatory to ensure coastal mangrove restoration initiatives and effective coastal management practices.

Journal name - Natural Hazards

Pawan Tiwari, A D Rao, Smita Pandey, and Vimlesh Pant

Published article is available online

Faculty Associated

Prof. A D Rao and Prof. Vimlesh Pant

An assessment of Indian Summer Monsoon (ISM) characteristics observed in continuous Radiosonding from New Delhi, National Capital Region (NCR-Delhi)

The geographical location of the New Delhi, National Capital Region (NCR) within the Northern Gangetic Plains is influenced by the Indian Summer Monsoon (ISM) and its associated weather. Monsoon weather is observed by the appearance of organised clouds, an outbreak of rainfall, and humid conditions. The current study examines pre-monsoon and monsoon weather conditions, together with intra-monsoonal variations (viz. wet, dry, and withdrawal spells) in wind structure, thermodynamic parameters, precipitation, and the structure of the Monsoon Boundary Layer (MBL). Investigations are also made into the macrophysical aspects of the monsoonal clouds/ weather systems that initiate and maintain ISM characteristics over the region. Moisture availability in the boundary layer carried by the low-level monsoon winds has a substantial role in forming monsoonal cloud mechanisms, such as shifting down the Lifting Condensation Level (LCL) closer to the Level of Free Convection (LFC) as an effect of continuous moisture supply from the lower levels and rising of Level of Neutral Buoyancy (LNB) near the tropopause region during the monsoonal period. Further influenced by the moisture in the boundary layer, the Precipitable Water (PW) is also reliant on the LCL height. Additionally, it is observed that the short-term zonal disturbances induced by extra-tropical and sub-tropical flows impacts intra-monsoonal weakening and variations. Analysis from several of the convective parameters and indices revealed that deep convective systems might occur throughout the monsoon season, while severe thunderstorms could develop during the pre-monsoon, and thunderstorms are less likely to happen during the withdrawal spell. The data presented here are the first to use continuous Radiosondes observations for quantitative assessments of the nature of the ISM over northern India.

Jerin Benny Chalakkal & Prof. Manju Mohan

Published article is available online

Faculty Associated

Prof. Manju Mohan

Interannual variability of coastal upwelling features along the eastern and western margins of the Arabian Sea

The Arabian Sea (AS), a basin in the North Indian Ocean (NIO), possesses strong coastal upwelling in its eastern and western margins. The spatio-temporal variability of upwelling over the Somalia, Oman and southwest coast of India is analyzed using observed data sets. The spectral analysis of upwelling indices highlight that the semi-annual and annual frequencies were dominant by two orders of magnitude compared with the inter-annual variability over these upwelling zones. The inter-annual variability of coastal upwelling was demarcated with variations in the causative forcing of upwelling associated with the positive modes of Indian Ocean Dipole (pIOD), El-Niño Southern Oscillation (El-Niño) and combined pIOD and El-Niño events over the southwest coast of India (SWCI) and Somalia. Along SWCI, the sea surface temperature and meridional wind stress weakened leading to warming associated with the suppression of upwelling during El-Niño, pIOD and combined events. Along Somalia, the upwelling favorable wind stress showed slight weakening due to pIOD and strong decline due to both El-Niño and combined modes. Along Oman, the upwelling favorable wind stress indicated negligible changes due to pIOD, increase due to El-Niño, and slight decline due to combined modes.

Fig: The JJAS SST anomaly (°C) composites for all years 1995-2018 (a), and for three types of events El-Niño (2002, 2004, 2009) (b), pIOD (2003, 2007, 2008, 2012) (c), combined El-Niño and pIOD modes (1997, 2006, 2015) (d). The anomalies were calculated with respect to the climatology of 1995-2018 and climate indices were calculated with respect to climatology of 1982-2020.

Tanuja Nigam & Vimlesh Pant

Published article is available online

Faculty Associated

Prof. Vimlesh Pant

A Mechanism for the Summer Monsoon Precipitation Variability Over Northwest India Driven by Moisture Deficit Transport

A large reservoir of saturation deficit air is known to exist over the northern Arabian Sea and the adjoining land regions during the peak of Indian summer monsoon (ISM). The strengthening of monsoon low-level jet (LLJ) in the northern parts of the Arabian Sea during the break phase of ISM helps in transporting this dry air toward northwestern India. Here, we show that, a weakening (strengthening) of the zonal flow over the northern Arabian Sea can reduce (enhance) the influx of the unsaturated air to the Northwest India and thereby enhance (reduce) precipitation there. The variability in the zonal flow over the northern Arabian Sea is a direct geostrophic response to the variability in the meridional pressure gradient over the Northwest India. The interannual variability in the mean sea level pressure over the region explains the inter-annual variability of ISM precipitation during July–August over northwestern India. The contribution of El Niño Southern Oscillation in the interannual variability of precipitation over this region is not significant.

Rahul Singh, S. Sandeep

Published article is available online

Faculty Associated

Prof. Sandeep Sukumaran

Modelling ambient PM2.5 exposure at an ultra-high resolution and associated health burden in megacity Delhi: exposure reduction target for 2030

Deriving hyperlocal information about fine particulate matter (PM2.5) is critical for quantifying exposure disparities and managing air quality at neighborhood scales in cities. Delhi is one of the most polluted megacities in the world, where ground-based monitoring was limited before 2017. Here we estimate ambient PM2.5 exposure at 100 m × 100 m spatial resolution for the period 2002–2019 using the random forest model. The model-predicted daily and annual PM2.5 show a ten-fold cross-validation R2 of 0.91 and 0.95 and root mean square error of 19.3 and 9.7 μg m−3, respectively, against coincident ground measurements from the Central Pollution Control Board ground network. Annual mean PM2.5 exposure varied in the range of 90–160 μg m−3 in Delhi, with shifts in local hotspots and a reduction in spatial heterogeneity over the years. Mortality burden attributable to ambient PM2.5 in Delhi increased by 49.7% from 9188 (95% uncertainty interval, UI: 6241–12 161) in 2002 to 13,752 (10,065–19,899) in 2019, out of which only 16% contribution was due to the rise in PM2.5 exposure. The mortality burden in 2002 and 2019 are found to be higher by 10% and 3.1%, respectively, for exposure assessment at 100 m scale relative to the estimates with 1 km scale. The proportion of diseases in excess mortality attributable to ambient PM2.5 exposure remained similar over the years. Delhi can meet the United Nations Sustainable Development Goal 3.4 target of reducing the non-communicable disease burden attributable to PM2.5 by one-third in 2030 relative to 2015 by reducing ambient PM2.5 exposure below the World Health Organization’s first interim target of 35 μg m−3. Our results demonstrate that machine learning can be a useful tool in exposure modelling and air quality management at a hyperlocal scale in cities.

Shashi Tiwari, Alok Kumar, Supriya Mantri and Sagnik Dey

Published article is available online

Faculty Associated

Prof Sagnik Dey

A cross-sectional analysis of ambient fine particulate matter (PM2.5) exposure and haemoglobin levels in children aged under 5 years living in 36 countries

Low haemoglobin (Hb) concentrations and anaemia in children have adverse effects on development and functioning, some of which may have consequences in later life. Exposure to ambient air pollution is reported to be associated with anaemia, but there is little evidence specific to low- and middle-income countries (LMICs), where childhood anaemia prevalence is greatest. We aimed to determine if long-term ambient fine particulate matter (≤2.5 μm in aerodynamic diameter [PM2.5]) exposure was associated with Hb levels and the prevalence of anaemia in children aged <5 years living in 36 LMICs. We used Demographic and Health Survey data, collected between 2010 and 2019, which included blood Hb measurements. Satellite-derived estimates of annual average PM2.5 was the main exposure variable, which was linked to children's area of residence. Anaemia was defined according to standard World Health Organization guidelines (Hb < 11 g/dL). The association of PM2.5 with Hb levels and anaemia prevalence was examined using multivariable linear and logistic regression models, respectively. We examined whether the effects of ambient PM2.5 were modified by a child's sex and age, household wealth index, and urban/rural place of residence. Models were adjusted for relevant covariates, including other outdoor pollutants and household cooking fuel. The study included 154,443 children, of which 89,904 (58.2%) were anaemic. The country-level prevalence of anaemia ranged from 15.8% to 87.9%. Mean PM2.5 exposure was 33.0 (±21.6) μg/m3. The adjusted model showed that a 10 μg/m3 increase in annual PM2.5 concentration was associated with greater odds of anaemia (OR = 1.098 95% CI: 1.087, 1.109). The same increase in PM2.5 was associated with a decrease in average Hb levels of 0.075 g/dL (95% CI: 0.081, 0.068). There was evidence of effect modification by household wealth index and place of residence, with greater adverse effects in children from lower wealth quintiles and children in rural areas. Exposure to annual PM2.5 was cross-sectionally associated with decreased blood Hb levels, and greater risk of anaemia, in children aged <5 years living in 36 LMICs.

Odo, D., I. A. Yang, S. Dey, M. S. Hammer, A. van Donkelaar, R. V. Martin, G.-H. Dong, B-Y. Yang, P. Hystad, L. D. Knibbs

Published article is available online

Faculty Associated

Prof. Sagnik Dey

How will climate change affect ambient air pollution and what can policy-makers do now? Lessons from India

Air pollution is a growing concern in India, and its adverse health effects are well documented. Climate change is likely to exacerbate this problem by altering weather patterns and increasing the frequency and severity of extreme events. This paper examines the potential impact of climate change on ambient air pollution in India and its implications for policy design. Our analysis reveals that pollution in India is highly sensitive to variation in weather, particularly in the densely populated Indus-Gangetic Plain. Using our estimated relationship between weather and pollution, we predict that changing weather patterns will increase average PM2.5 concentrations by 3.1 µg/m3, leading to a loss of 364 million years of life expectancy. To address this challenge, we propose an emissions fee calibrated to be highest in regions most vulnerable to persistently high levels of pollution and most sensitive to future deterioration in air quality due to climate change.

Avraham Ebenstein, Sangeeta Bansal, Sagnik Dey, Tanya Gupta, Kshitij Abhay Kakade, Avi Simhon

Published article is available online

Faculty Associated

Prof. Sagnik Dey

India's photovoltaic potential amidst air pollution and land constraints

India aims for ambitious solar energy goal to fulfil its climate commitment but there are limited studies on solar resource assessment considering both environmental and land availability constraints. The present work attempts to address this issue using satellite-derived air pollution, radiation, and land use data over the Indian region. Surface insolation over India has been decreasing at a rate of 0.29 G 0.19 Wm-2 y1 between 2001 and 2018. Solar resources over nearly 98%, 40%, and 39% of the Indian landmass are significantly impacted by aerosols, clouds, and both aerosols and clouds respectively. Only 29.3% of the Indian landmass is presently suitable for effective solar photovoltaic harnessing, but this is further declining by 0.21% annually, causing a presumptive loss of 50 GW solar potential, translating 75 TWh power generation. Lowering two decades of aerosol burden can make 8% additional landmass apt for photovoltaic use. Alleviating aerosol-induced dimming can fast-track India’s solar energy expansion.

Ghosh, S., A. Kumar, D. Ganguly and S. Dey

Published article is available online

Faculty Associated

Prof. Dilip Ganguly and Prof. Sagnik Dey

Cumulative effect of PM2.5 components is larger than the effect of PM2.5 mass on child health in India

While studies on ambient fine particulate matter (PM2.5) exposure effect on child health are available, the differential effects, if any, of exposure to PM2.5 species are unexplored in lower and middle-income countries. Using multiple logistic regression, we showed that for every 10 μg m−3 increase in PM2.5 exposure, anaemia, acute respiratory infection, and low birth weight prevalence increase by 10% (95% uncertainty interval, UI: 9–11), 11% (8–13), and 5% (4–6), respectively, among children in India. NO3-, elemental carbon, and NH4+ were more associated with the three health outcomes than other PM2.5 species. We found that the total PM2.5 mass as a surrogate marker for air pollution exposure could substantially underestimate the true composite impact of different components of PM2.5. Our findings provide key indigenous evidence to prioritize control strategies for reducing exposure to more toxic species for greater child health benefits in India.

Ekta Chaudhary, Franciosalgeo George, Aswathi Saji, Sagnik Dey, Santu Ghosh, Tinku Thomas, Anura. V. Kurpad, Sumit Sharma, Nimish Singh, Shivang Agarwal & Unnati Mehta

Published article is available online

Faculty Associated

Prof. Sagnik Dey

An environmental justice analysis of air pollution in India

Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5levels corresponding to 0.127 μg/m3 (95% CI 0.062 μg/m3, 0.192 μg/m3) and 0.199 μg/m3(95% CI 0.116 μg/m3, 0.283 μg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters.

Priyanka N. deSouza, Ekta Chaudhary, Sagnik Dey, Soohyeon Ko, Jeremy Németh, Sarath Guttikunda, Sourangsu Chowdhury, Patrick Kinney, S. V. Subramanian, Michelle L. Bell & Rockli Kim

Published article is available online

Faculty Associated

Prof. Sagnik Dey

Modelling PM2.5 for Data-Scarce Zone of Northwestern India using Multi Linear Regression and Random Forest Approaches

PM2.5 (Particulate matter with aerodynamic diameter <2.5 m) concentrations above permissible limit causes air quality deterioration and hampers human health. Due to the lack of a good spatial network of ground-based PM monitoring sites and systematic checking, the availability of continuous data of PM2.5 concentrations at macro and meso scales is restricted. Present research estimated PM2.5 concentrations at high (1 km) resolution over Faridabad, Ghaziabad, Gurugram and Gautam Buddha Nagar, a data-scarce zone of the highly urbanized area of northwestern India for the year 2019 using Random Forest (RF), Multi-Linear Regression (MLR) models and Hybrid Model combining RF and MLR. It included Aerosol Optical Depth (AOD), meteorological data and limited in-situ data of PM2.5. For validation, the correlation coefficient (R), Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE) and Relative Prediction Error (RPE) have been utilized. The hybrid model estimated PM2.5 with a greater correlation (R = 0.865) and smaller RPE (22.41%) compared to standalone MLR/RF models. Despite the inadequate in-situ data, Greater Noida has been found to have a high correlation (R = 0.933) and low RPE (32.13%) in the hybrid model. The most polluted seasons of the year are winter (137.28 µgm−3) and post-monsoon (112.93 µgm−3), whereas the wet monsoon (44.56 µgm−3) season is the cleanest. The highest PM2.5 level was recorded in Noida followed by Ghaziabad, Greater Noida and Faridabad. The findings of the present research will provide an input dataset for air pollution exposure risk research in parts of northwestern India with sparse monitoring data.

Sharma, V., S. Ghosh, S. Dey, and S. Singh

Published article is available online

Faculty Associated

Prof. Sagnik Dey

Addressing Biases in Ambient PM2.5 Exposure and Associated Health Burden Estimates by Filling Satellite AOD Retrieval Gaps over India

Ambient PM2.5 exposure statistics in countries with limited ground monitors are derived from satellite aerosol optical depth (AOD) products that have spatial gaps. Here, we quantified the biases in PM2.5 exposure and associated health burden in India due to the sampling gaps in AOD retrieved by a Moderate Resolution Imaging Spectroradiometer. We filled the sampling gaps and derived PM2.5 in recent years (2017-2022) over India, which showed fivefold cross-validation R2 of 0.92 and root mean square error (RMSE) of 11.8 μg m-3 on an annual scale against ground-based measurements. If the missing AOD values are not accounted for, the exposure would be overestimated by 19.1%, translating to an overestimation in the mortality burden by 93,986 (95% confidence interval: 78,638-110,597) during these years. With the gap-filled data, we found that the rising ambient PM2.5 trend in India has started showing a sign of stabilization in recent years. However, a reduction in population-weighted exposure balanced out the effect of the increasing population and maintained the mortality burden attributable to ambient PM2.5 for 2022 (991,058:798,220-1,183,896) comparable to the 2017 level (1,014,766:812,186-1,217,346). Therefore, a decline in exposure alone is not sufficient to significantly reduce the health burden attributable to ambient PM2.5 in India.

Varun Katoch, Alok Kumar, Fahad Imam, Debajit Sarkar, Luke D. Knibbs, Yang Liu, Dilip Ganguly, and Sagnik Dey

Published article is available online

 

Faculty Associated

Prof. Dilip Ganguly and Prof. Sagnik Dey

Atmospheric elemental carbon pollution and its regional health disparities in China

Previous studies have reported that atmospheric elemental carbon (EC) may pose potentially elevated toxicity when compared to total ambient fine particulate matter (PM2.5). However, most research on EC has been conducted in the US and Europe, whereas China experiences significantly higher EC pollution levels. Investigating the health impact of EC exposure in China presents considerable challenges due to the absence of a monitoring network to document long-term EC levels. Despite extensive studies on total PM2.5 in China over the past decade and a significant decrease in its concentration, changes in EC levels and the associated mortality burden remain largely unknown. In our study, we employed a combination of satellite remote sensing, available ground observations, machine learning techniques, and atmospheric big data to predict ground EC concentrations across China for the period 2005–2018, achieving a spatial resolution of 10 km. Our findings reveal that the national average annual mean EC concentration has remained relatively stable since 2005, even as total PM2.5 levels have substantially decreased. Furthermore, we calculated the all-cause non-accidental deaths attributed to long-term EC exposure in China using baseline mortality data and pooled mortality risk from a cohort study. This analysis unveiled significant regional disparities in the mortality burden resulting from long-term EC exposure in China. These variations can be attributed to varying levels of effectiveness in EC regulations across different regions. Specifically, our study highlights that these regulations have been effective in mitigating EC-related health risks in first-tier cities. However, in regions characterized by a high concentration of coal-power plants and industrial facilities, additional efforts are necessary to control emissions. This observation underscores the importance of tailoring environmental policies and interventions to address the specific challenges posed by varying emission sources and regional contexts.

Published article is available online

Faculty Associated

Prof. Sagnik Dey

Excess mortality risk due to heat stress in different climatic zones of India

India is at a high risk of heat stress-induced health impacts and economic losses owing to its tropical climate, high population density, and inadequate adaptive planning. The health impacts of heat stress across climate zones in India have not been adequately explored. Here, we examine and report the vulnerability to heat stress in India using 42 years (1979–2020) of meteorological data from ERA-5 and developed climate-zone-specific percentile-based human comfort class thresholds. We found that the heat stress is usually 1–4 °C higher on heatwave (HW) days than on nonheatwave (NHW) days. However, the stress on NHW days remains considerable and cannot be neglected. We then showed the association of a newly formulated India heat index (IHI) with daily all-cause mortality in three cities – Delhi (semiarid), Varanasi (humid subtropical), and Chennai (tropical wet and dry), using a semiparametric quasi-Poisson regression model, adjusted for nonlinear confounding effects of time and PM2.5. The all-cause mortality risk was enhanced by 8.1% (95% confidence interval, CI: 6.0–10.3), 5.9% (4.6–7.2), and 8.0% (1.7–14.2) during “sweltering” days in Varanasi, Delhi, and Chennai, respectively, relative to “comfortable” days. Across four age groups, the impact was more severe in Varanasi (ranging from a 3.2 to 7.5% increase in mortality risk for a unit rise in IHI) than in Delhi (2.6–4.2% higher risk) and Chennai (0.9–5.7% higher risk). We observed a 3–6 days lag effect of heat stress on mortality in these cities. Our results reveal heterogeneity in heat stress impact across diverse climate zones in India and call for developing an early warning system keeping in mind these regional variations.

Rohit Kumar Choudhary, Pallavi Joshi, Santu Ghosh, Dilip Ganguly, Kalpana Balakrishnan, Nidhi Singh, Rajesh Kumar Mall, Alok Kumar, and Sagnik Dey

Published article is available online

Faculty Associated

Prof. Sagnik Dey and Prof. Dilip Ganguly

High ambient air pollution erodes the benefits of using clean cooking fuel in preventing low birth weight in India

A large fraction of the population in rural India continues to use biomass fuel for cooking and heating. In-utero exposure to the resulting household air pollution (HAP), is known to increase the risk of low birth weight (LBW). Mitigating HAP, by shifting to clean cooking fuel (CCF), is expected to minimize the risk associated with LBW. However, India also has high levels of ambient air pollution (AAP). Whether exposure to AAP modifies the effect of reducing HAP by switching to CCF on LBW is not known. The present study addressed this knowledge gap by analyzing the National Family Health Survey (2019–21) data of the most recent full-term, singleton, live births from rural households born after 2017 (n = 56 000). In-utero exposure to AAP was calculated from satellite-derived ambient fine particulate matter (PM2.5) concentration at the level of the primary sampling unit for the pregnancy duration of the mothers. The moderation by ambient PM2.5 level on the odds of LBW among CCF users was examined by logistic regression analysis with interaction. The adjusted odds ratio (aOR) of LBW was 7% lower among users of CCF. At the lowest Decile (20–37 μg m−3) of ambient PM2.5 exposure, the aOR of LBW among CCF users was 0.83 (95% CI:0.81–0.85). At every 10th percentile increase in ambient PM2.5 exposure (in the range 21–144 μg m−3), aOR increased gradually, reaching the value of 1 at PM2.5 level of 93 μg m−3. Our results, therefore, suggest that the benefit of using CCF during pregnancy may be downgraded by moderate to high ambient PM2.5 exposure.

Ritu Parchure5,1, Ekta Chaudhary2, Shrinivas Darak1, Santu Ghosh3, Alok Kumar2 and Sagnik Dey2,4

Published article is available online

Faculty Associated

Prof. Sagnik Dey

Climate Change Will Affect Fire Weather Danger in Indian Forests: Study

New Delhi: Human activity is causing the earth’s climate to change in unprecedented ways. Atmospheric temperatures are rising rapidly and will continue to rise in the future. These warming temperatures will increase the fire weather danger in many Indian forests, according to a recent study by IIT Delhi.

IIT Delhi researchers developed a very high-resolution data set of future climate projections and used that data to calculate the Fire Weather Index (FWI) for forest regions of India. The results showed that forests in Central and South India and the Himalayan region will see significant increases in FWI by the end of the century. The fire season in these regions will also increase by 12-61 days.

These findings align well with the conventional wisdom that higher temperatures increase forest fire hazard. Interestingly, the study showed that not to be the case in all forests. Humid tropical forests in the Western Ghats and parts of the North-East, where rainfall and humidity are projected to rise, will experience lower FWI despite the warming.

Dr. Somnath Baidya Roy, Professor and Head of the Centre for Atmospheric Sciences, and a co-author of the study, said, “We must study forest fires in India at a high degree of granularity to properly represent the diversity in climate and forest types across the country. Course resolution global scale studies simply don’t work for us.”

Anasuya Barik, PhD student at the Centre for Atmospheric Sciences and the lead author of the study, said, “Our study is the first of its kind in India and has significant implications for understanding and managing forest fires. Our study shows that we need to develop fire danger thresholds and management policies at local levels instead of national levels.”

The study was published in Communications Earth and Environment, a highly ranked journal from the Nature Springer group and is available online at https://www.nature.com/articles/s43247-023-01112-w.

Faculty Associated

Prof. Somnath Baidya Roy

Carbon fluxes in spring wheat agroecosystems in India

Carbon fluxes from agroecosystems contribute to the variability of the carbon cycle and atmospheric [CO2]. In this study, we look at the carbon fluxes and their drivers in a spring wheat agroecosystem using ISAM land surface model. We validated the model on a regional scale by comparing modeled leaf area index (LAI) and yield against site-scale observations and regional datasets. ISAM-simulated carbon fluxes were validated against an experimental spring wheat site at IARI for the growing season of 2013–2014. Additionally, we compared with the published carbon flux data and found that ISAM captures the seasonality well. Following that, regional-scale runs were performed. The results revealed that fluxes vary significantly across regions, primarily owing to differences in planting dates. Numerical experiments were conducted to investigate how natural forcings such as changing temperature and [CO2] levels as well as agricultural management practices such as nitrogen fertilization and water availability could contribute to the rising carbon flux trends. The experiments revealed that increasing [CO2], nitrogen fertilization, and irrigation water contributed to increased carbon fluxes, with nitrogen fertilization having the most significant effect.

Published article is available online

Faculty Associated

Dr Somnath Baidya Roy

Characterizing the regional XCO2 variability and its association with ENSO over India inferred from GOSAT and OCO-2 satellite observations

India is primarily concerned with comprehending regional carbon source-sink response in the context of changes in atmospheric CO2 concentrations or anthropogenic emissions. Recent advancements in high-resolution satellite's fine-scale XCO2 measurements provide an opportunity to understand unprecedented details of source-sink activity on a regional scale. In this study, we investigated the long-term variations of XCO2 concentration and growth rates as well as its covarying relationship with ENSO and regional climate parameters (temperature, precipitation, soil moisture, and NDVI) over India from 2010 to 2021 using GOSAT and OCO-2 retrievals. The results show since the launch of OCO-2 in 2014, the number of monthly high-quality XCO2 soundings over India has grown nearly 100-fold compared to GOSAT, launched in 2009. Also, the discrepancy in XCO2 increase of 2.54(2.43) ppm/yr was observed in GOSAT (OCO-2) retrieval during an overlapping measurement period (2015–2021). Additionally, wavelet analysis indicated that the OCO-2 retrieval is able to capture a better frequency of local-scale XCO2 variability compared to GOSAT, owing to its high-resolution cloud-free XCO2 soundings, providing more well-defined regional-scale source-sink features.

Chiranjit Das (PhD scholar, CAS), Dr Ravi Kumar Kunchala (Asst Prof, CAS), and their collaborators’ article in Science of the Total Environment.

Published article is available online

Faculty Associated

Prof Ravi Kumar Kunchala

Impact of AWiFS derived land use/land cover over the intensely urbanised domain of National Capital Region (NCR) - Delhi in simulating monsoon weather

Land-Use/Land-Cover (LULC) plays a crucial role in meteorological models because they determine the crustal properties that interfere with the exchange of energy, momentum, and moisture between the land surface and the atmosphere. The current study integrates a legitimate ground-truth LULC based on the Advanced Wide Field Sensor (AWiFS) in the Weather Research and Forecasting (WRF) model framework for simulating short-term monsoon weather over the National Capital Region (NCR Delhi), the biggest urban settlement in the Monsoon Zone. AWiFS provides LULC data at 56 m spatial resolution. The newly implemented AWiFS LULC precisely distinguishes the WRF model default Moderate Resolution Imaging Spectroradiometer (MODIS) classification and urban representation. Both simulations most well capture the urban meteorological influence over the urban core. However, the AWiFS more accurately depicts NCR urban Land Use (LU), highlighted in the spatial and Probability Density Function (PDF) distributions of various meteorological variables, and captures numerous small-scale suburban settlements outside the central core zone. The impact of the omission of the Noida suburban in MODIS LU is also notable in the planetary boundary layer (PBL). Convective parameters and precipitation correlate strongly, and the AWiFS simulation considerably enhanced Convective Available Potential Energy (CAPE), Convective Inhibition (CIN) and other associated variables. Further, it indicates that a higher magnitude (>200 J kg−1 in difference) of CAPE is simulated over Faridabad, likely an urban-induced downwind effect due to the accurate representation of the Noida suburban in AWiFS. The study highlights the importance of updated LULC in enhancing model realisation, and AWiFS-based simulation showed improved performance.

Published article is available online

Faculty Associated

Prof. Manju Mohan

Climate science to inform adaptation policy: Heat waves over India in the 1.5°C and 2°C warmer worlds

Developing a better scientific understanding of anthropogenic climate change and climate variability, especially the prediction/projection of climate futures with useful temporal and geographical resolution and quantified uncertainties, and using that knowledge to inform adaptation planning and action will become crucially important in the coming years. Generating such policy-relevant knowledge may be particularly important for developing countries such as India. It is with this backdrop that, in this paper, we analyze future heat waves in India by using observations and a large number of model simulations of historical, + 1.5 °C, and + 2.0 °C warmer worlds. In both the future scenarios, there is an increased probability of heat waves during June and July when the Indian monsoon is in full swing and humidity is high, which makes the heat events even more of a health risk. While the highest temperatures in heat waves may not increase much in future climates, the duration and areal extent of the heat waves will most likely increase, leading to the emergence of new heat wave-prone zones in India. The results indicate that the joint frequencies of the longest duration and large area events could be nearly threefold greater in the + 1.5 °C and fivefold greater in the + 2.0 °C future scenarios compared to historical simulations. Thus, overall, the study indicates a substantial increase in the risk of heat events that typically elicit warnings from forecasters. The likely widespread and persistent nature of heat wave events in the future, as revealed by this study, will require planning and adaptation measures beyond the short-term disaster planning frameworks currently in place. Exploring what these measures might look like is beyond the scope of this study, but it underlines the importance of developing climate knowledge with high temporal and geographical resolution capable of informing adaptation policy and planning.

Faculty Associated

Prof. Krishna AchutaRao

Monsoon Prediction for 2023

Artificial Intelligence and Machine Learning models are employed to predict the All India Summer Monsoon Rainfall (AISMR) for 2023. The AISMR will be ~790mm in the oncoming 2023 summer season, which is typical of a normal monsoon year. The data driven models are trained with historical AISMR data, Niño3.4 index data and categorical IOD data, which show promising results. These emerging techniques from data science can complement the existing physical models in monsoon prediction.

Note: Please note that the above information is only for academic and research purposes. The data driven models have been recently developed and are currently under trial.

Update:

This prediction was made in the month of January and it turns out to be true, when validated with the actual observations after the end of this year’s monsoon with a marginal error of ~3%. The research work has been recently published in the journal Scientific Reports (Narang et al. 2023, DOI: 10.1038/s41598-023-44284-3) and the findings were reported by several leading newspapers.

Press release by IITD https://home.iitd.ac.in/show.php?id=40&in_sections=Research

Faculty Associated

Prof. S. K. Mishra

Climatology of Rossby Wave Breaking Over the Subtropical Indian Region

Plain Language Summary : Rossby waves are planetary-scale waves that usually propagate over higher latitude regions. Sometimes, these waves break similar to waves breaking at the shoreline. This results in air mass transport from high latitudes to low latitudes and vice-versa. Also, the wave breaking destabilizes the underlying atmosphere and triggers extreme weather conditions. In this study, we have implemented an algorithm suitable to the subtropical Indian region to detect the Rossby Wave Breaking (RWB) events. The algorithm uses the high Potential vorticity (PV) contours which determine the flow of the upper atmosphere to identify the breaking events. Using the algorithm, we have detected more than 500 RWB events during the period 1979–2021 and reported the spatiotemporal variability of the RWB events over the study region. The results suggest that the number of events per year has increased in the last two decades. In addition, the vertical and latitudinal intrusions of the RWB events along with its wave properties suggest that the breakings are stronger in winter compared to other seasons. The RWB events trigger extreme weather events such as extreme rainfall events and these events are modulated by the conditions in the Pacific.

Published article is available online

Faculty Associated

Prof. Ravi Kumar Kunchala

National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake

Plain Language Summary: Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. This paper describes a pilot dataset of country-specific net carbon exchange and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” net carbon exchange outputs from the v10 Orbiting Carbon Observatory (OCO-2 satellite) Modeling Intercomparison Project. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.

Published article is available online

Press release by NASA, JPL and Royal Belgian Institute for Space Aeronomy

Dataset here and Data visualizations here

Faculty Associated

Prof. Sajeev Philip

Automated Machine Learning to Evaluate the Information Content of Tropospheric Trace Gas Columns for Fine Particle Estimates Over India: A Modeling Testbed

Plain Language Summary : Ground-level fine particle (PM2.5) concentrations are frequently estimated with freely available satellite Aerosol Optical Depth (AOD) products. We focus on India where sparse ground-based monitoring leaves gaps in our understanding of particle concentrations and the relative importance of different sources. We use an atmospheric chemistry model to test whether satellite retrievals of tropospheric trace gas columns can provide information on the origins of PM2.5 and improve satellite-derived PM2.5. We created an Automated Machine Learning workflow to evaluate the utility of incorporating multiple trace gas columns in PM2.5 estimates, which represents nonlinear relationships between predictands and predictors while freeing users from selecting and tuning a specific machine learning model. On daily and monthly time scales, we quantify the relative information content of trace gas columns, AOD, meteorological fields, and emissions. We find that incorporating trace gas columns improves PM2.5 estimates and may also enable inference of broad characteristics of particle composition.

Published article is available online

Faculty Associated

Prof. Sagnik Dey

Spatio-temporal patterns of tropospheric NO2 over India during 2005–2019

Plain Language Summary : Most of the studies focusing on air pollution problem in India deal with PM2.5 or PM10­. Gaseous pollutants that are precursor to PM are not extensively studied. In this work, the major NO2 hotspots are identified and the shifts of the hotspots over the last 15 years are examined. Anthropogenic activities in urban-rural settlement are found to elevate tropospheric NO2 by 11-40%.

Published article is available online

Faculty Associated

Prof. Sagnik Dey

A cross-sectional analysis of long-term exposure to ambient air pollution and cognitive development in children aged 3-5 years living in 12 low- and middle-income countries

Plain Language Summary : While studies have shown the negative impact of air pollution on child health, the effect on early cognitive development is scarcely studied. Here the association of ambient PM2.5 and cognitive delay in 12 low- and middle-income countries are examined. It has been found that early-life exposure to ambient PM2.5 causes delay in cognitive development of children, with the effect being higher in urban children. Reducing air pollution exposure would lead to considerable health benefits for the children.

Published article is available online

Faculty Associated

Prof. Sagnik Dey

A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China

Plain Language Summary : Epidemiological studies mostly focused on exposure to PM2.5 mass. There is not enough evidence on differential impacts of PM2.5 species on health. In this study, a satellite-driven machine earning model has been developed to develop a sulfate exposure data for China. Further analysis reveals that sulfate exposure in China declined by 28.7% from 2013 to 2018 after the implementation of air pollution control strategies. As a result, the non-accidental and cardiopulmonary deaths attributed to sulfate decreased by 40.7% and 42.3% during the period, respectively. The study has implications for other developing countries plagued by air pollution.

Published article is available online

Faculty Associated

Prof. Sagnik Dey

Aerosol radiative feedback enhances particulate pollution over India: A process understanding

Plain Language Summary : Understanding aerosol processes is important to isolate the relative contributions of chemistry and dynamics on air pollution. In this work, the direct aerosol radiative feedback on the meteorology has been examined using a coupled chemistry model. The feedback processes are found to elevate the primary aerosol concentrations in India, while the effect reduces O3 in most of the regions. The results imply that reducing primary aerosol emission would lead to a greater reduction in PM2.5 levels, particularly in the polluted regions.

Published article is available online

Faculty Associated

Prof. Sagnik Dey

Is the Monsoon climatology observed over the National Capital Region, Delhi indicative of an Urban -Monsoon linkage on rainfall modification?

Plain Language Summary: The Indian Summer Monsoon (ISM) plays a vital role in the lives of the National Capital Region (NCR) - Delhi, the second biggest urban settlement globally. This study attempt to assess the spatio-temporal characters and decadal changes of monsoonal features in detail. The climatology of Monsoonal rainfall and Monsoon Low Level Jet (MLLJ) are investigated. NCR Delhi exhibits a ∼ 28.5% in the recent decade and ∼ 19.7% in the 1997–06 decade reduction in rainfall is noticed compared with 1987–96 decade. The study reveals that the precipitation reduction is alongside the weakening of the MLLJ and marginal reduction of core height over the region. Also, the magnitude of surface wind declined with the directional shift of both MLLJ and surface winds notable in southerlies in the 2007–16 decade. Rainfall islands parallel to the urban heat islands (UHI) are seen likely as coupled monsoon-urban induced effects under the weakened synoptic regime evidenced through MLLJ. The Land Surface Temperature difference (UHI) of ∼ 2.5 °C and more than 3.5 °C is observed from the urban centre to the surrounding cropland during the months of August (core monsoon) and September, respectively. In addition to the rainfall islands during August, islands of rainfall in both the upwind and downwind directions of the urban centre are noted in September. The rainfall patterns point out the signal of monsoon-rainfall modification due to NCR urbanization. Further, the influence of crosswinds with Aravali ridges and the presence of Himalayan foothills are also notable in the NCR rainfall patterns.

Faculty Associated

Prof. Manju Mohan

Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates

Plain Language Summary: In this work we review the estimates of excess mortality attributable to outdoor air pollution at the global scale, by comparing studies available in the literature. We find large differences between the estimates, mainly caused by mathematical function used to describe the pollution-health link, as well as the number of health outcomes included in the calculations. We showed that, despite the considerable advancements in our understanding of health impacts of air pollution, the precision of the estimates has not increased in the last decades. We offer recommendations for future measurements and research directions, which will help to improve our understanding and quantification of air pollution-health relationships.

Published article is available online

Faculty Associated

Prof. Sagnik Dey

Projected Climate Variability of Internal Waves in the Andaman Sea

Plain Language Summary: The Andaman Sea, in the northeast Indian Ocean, is renowned for large-amplitude internal waves. Here, we use a global climate model (CanESM5) to investigate the long-term variability of internal waves in the Andaman Sea under a range of shared socioeconomic pathway (SSP) scenarios. SSPs are future societal development pathways related to emissions and land use scenarios. We project that mean values of depth-averaged stratification will increase by approximately 6% (SSP1-2.6), 7% (SSP2-4.5), and 12% (SSP5-8.5) between 1871-1900 and 2081-2100. Simulating changes in internal tides between the present (2015-2024) and the end-century (2091-2100), we find that the increase in stratification will enhance internal tide generation by approximately 4 to 8%. We project that the propagation of internal tides into the Andaman Sea and the Bay of Bengal will increase by 8 to 18% and 4 to 19%, respectively, under different SSP scenarios. Such changes in internal tides under global warming will have implications for primary production and ecosystem health not only in the Andaman Sea but also in the Bay of Bengal.

Published article is available online

Faculty Associated

Prof. A. D. Rao

Reducing the Burden of Anaemia in Indian Women of Reproductive Age with Clean-Air Targets

Plain Language Summary: India has one of the highest (53%) global prevalences of anaemia among women of reproductive age (WRA, 15–49 years). Long-term exposure to ambient fine particulate matter (PM2.5), a type of air pollution, may increase the prevalence of anaemia through systemic inflammation. Using a linear mixed model adjusted for potential confounding factors, we show that for every 10 µg m−3 increase in ambient PM2.5 exposure, the average anaemia prevalence among Indian WRA increases by 7.23% (95% uncertainty interval, 6.82–7.63). Among PM2.5 species, sulfate and black carbon are more associated with anaemia than organics and dust. Among sectoral contributors, industry was the greatest, followed by the unorganized, domestic, power, road dust, agricultural waste burning and transport sectors. If India meets its recent clean-air targets, such anaemia prevalence among WRA will fall from 53% to 39.5%, taking 186 districts below the national target of 35%. Our results suggest that the transition to clean energy would accelerate India’s progress towards the ‘anaemia-free’ mission target.

Published article is available online

Faculty Associated

Prof. Sagnik Dey

Weakening of Indian Summer Monsoon Synoptic Activity in Response to Polar Sea Ice Melt Induced by Albedo Reduction in a Climate Model

Plain Language Summary: The sea ice is melting rapidly in a warming climate, which can have feedback effects on the climate system. However, the impact of sea ice melt on low latitude climate is not adequately understood. The Indian summer monsoon (ISM), known as the lifeline of South Asia, is essential to the water security of more than 1.5 billion people. We examined the response of the ISM to the polar sea ice melt using a suite of global climate model experiments. Our simulations show that the monsoon circulation and rainfall weaken substantially due to the sea ice melt. Further, the number of propagating precipitating vortices embedded in the monsoon circulation declined by about 22% in the sea ice melt experiments. Our results suggest that the Arctic and Antarctic sea ice melt can have severe implications for the water security of South Asia.

Published article is available online

Faculty Associated

Prof. Sandeep Sukumaran

Meridional Propagation of Carbon Dioxide (CO2) Growth Rate and Flux Anomalies From the Tropics Due to ENSO

Plain Language Summary: El Niño Southern Oscillation (ENSO) is a dominant tropical climate mode that has uneven impact on carbon dioxide (CO2) growth rates in different parts of the globe. In this study, we show an asymmetric meridional evolution of ENSO signal imprinted on CO2 growth rate using observations and chemistry-transport model simulations. A time delay of 8 and 4 months between ENSO and CO2 growth rate are found near northern and southern high latitudes. This propagation asymmetry near the surface is linked to asymmetry in poleward transport of airmass and dominance of southern tropical flux variability to ENSO. The propagation is more homogenous in the upper atmosphere, where the flux signals are mixed. The terrestrial biosphere flux and fire emissions are reiterated as the underlying biophysical process in CO2 anomaly influenced by ENSO.

Published article is available online

Faculty Associated

Prof. Ravi Kumar Kunchala

Association between Acute Exposure to PM2.5 Chemical Species and Mortality in Megacity Delhi, India

ABSTRACT: The association between daily all-cause mortality and short-term fine particulate matter (PM2.5) exposure is well established in the literature. However, association between acute exposure to PM2.5 chemical species and mortality is not well known, especially in developing countries like India. Here we examined associations between mortality and acute exposure to PM2.5 mass concentration and their 15 chemical components using data from 2013 to 2016 in megacity Delhi using a semiparametric quasi-Poisson regression model, adjusting for mean temperature, relative humidity, and long-term time trend as the major potential confounders. Mortality estimates were further checked for effect modification by sex, age group, and season. The subspecies of NO3, NH4NO3, Cr, NH4+, EC, and OC showed a higher mortality impact than the total PM2.5 mass. Males were at higher risk from NO3, SO42–, and their NH4+ compounds along with carcinogen Cr, whereas female group was at higher risk from EC and OC. Among all age groups, the elderly above 65 years were the most vulnerable group prone to mortality effects from maximum species. The major mortality risk from all hazardous species arose from their winter exposures. Our study provides the first evidence of association between acute exposure to PM2.5 chemical species and mortality anywhere in India and recommends similar studies in other regions so that sectoral mitigation emitting the most toxic species can be prioritized to maximize the health benefits.

Published article is available online

Faculty Associated

Prof. Sagnik Dey

Address

Centre for Atmospheric Sciences, Block VI,
Indian Institute of Technology Delhi
Hauz Khas, New Delhi-110 016, India

Email
hodcas[at]admin[dot]iitd[dot]ac[dot]in
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