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
Prof. Sagnik Dey and Prof. Dilip Ganguly
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
Prof. Sagnik Dey
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.
Prof. Somnath Baidya Roy
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.
Dr Somnath Baidya Roy
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.
Prof Ravi Kumar Kunchala
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.
Prof. Manju Mohan
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.
Prof. Krishna AchutaRao
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.
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
Prof. S. K. Mishra
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.
Prof. Ravi Kumar Kunchala
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.
Prof. Sajeev Philip
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.
Prof. Sagnik Dey
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%.
Prof. Sagnik Dey
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.
Prof. Sagnik Dey
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.
Prof. Sagnik Dey
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.
Prof. Sagnik Dey
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.
Prof. Manju Mohan
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.
Prof. Sagnik Dey
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.
Prof. A. D. Rao
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.
Prof. Sagnik Dey
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.
Prof. Sandeep Sukumaran
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
Prof. Ravi Kumar Kunchala
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
Prof. Sagnik Dey