Effectiveness of respiratory face masks in reducing acute PM2.5 pollution exposure during peak pollution period in Delhi.

The cities of North India, such as Delhi, face a significant public health threat from severe air pollution. Between October 2021 and January 2022, 79 % of Delhi's daily average PM2.5 (Particulate matter with an aerodynamic diameter ≤ 2.5 µm) values exceeded 100 µg/m3 (the permissible level being 60 µg/m3 as per Indian standards). In response to this acute exposure, using Respiratory Face Masks (RFMs) is a cost-effective solution to reduce immediate health risks while policymakers develop long-term emission control plans. Our research focuses on the health and economic benefits of using RFMs to prevent acute exposure to PM2.5 pollution in Delhi for different age groups. Our findings indicate that, among the fifty chosen RFMs, M50 has greatest potential to prevent short-term excess mortality (908 in age ranges 5-44), followed by M49 (745) and M48 (568). These RFMs resulted in estimated economic benefits of 500.6 (46 %), 411.1 (37 %), and 313.4 (29 %) million Indian Rupee (INR), respectively during October-January 2021-22. By wearing RFMs such as M50, M49, and M48 during episodes of bad air quality, it is estimated that 13 % of short-term excess mortality and associated costs could be saved if at least 30 % of Delhi residents followed an alert issued by an operational Air Quality Early Warning System (AQEWS) developed by the Ministry of Earth Sciences. Our research suggests that RFMs can notably decrease health and economic burdens amid peak PM2.5 pollution in post-monsoon and winter seasons until long-term emission reduction strategies are adopted. It is suggested that an advisory may be crafted in collaboration with statutory bodies and should be disseminated to assist the vulnerable population in using RFMs during winter. The analysis presented in this research is purely science based and outcomes of study are in no way to be construed as endorsement of product.

Performance of high resolution (400 m) PM2.5 forecast over Delhi.

This study reports a very high-resolution (400 m grid-spacing) operational air quality forecasting system developed to alert residents of Delhi and the National Capital Region (NCR) about forthcoming acute air pollution episodes. Such a high-resolution system has been developed for the first time and is evaluated during October 2019-February 2020. The system assimilates near real-time aerosol observations from in situ and space-borne platform in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to produce a 72-h forecast daily in a dynamical downscaling framework. The assimilation of aerosol optical depth and surface PM2.5 observations improves the initial condition for surface PM2.5 by about 45 µg/m3 (about 50%).The accuracy of the forecast degrades slightly with lead time as mean bias increase from + 2.5 µg/m3 on the first day to - 17 µg/m3 on the third day of forecast. Our forecast is found to be very skillful both for PM2.5 concentration and unhealthy/ very unhealthy air quality index categories, and has been helping the decision-makers in Delhi make informed decisions.

Long-term impact of Amazon river runoff on northern hemispheric climate.

Amazon discharges a large volume of freshwater into the ocean, yet its impact on climate is largely unknown. Climate projections show that a warmer northern tropical Atlantic Ocean together with a warmer equatorial Pacific lead to extreme droughts in the Amazonia, considerably reducing the Amazon runoff. Here we present results from coupled model simulations and observations on the climatic response to a significant reduction in Amazon runoff into the Atlantic Ocean. Climate model simulation without Amazon runoff resulted in cooler equatorial Atlantic, weakening the Hadley cell and thereby the atmospheric meridional cells. Consequently, the extratropical westerlies turned weaker, leading to prevalent negative North Atlantic Oscillation (NAO) like climate, similar to the observed anomalies during Amazon drought years. This study reaffirms that spatial signature of NAO is in part driven by sea surface temperature (SST) anomalies in the tropical Atlantic. Winters of northern Europe and eastern Canada turned cooler and drier whereas southern Europe and the eastern United States experienced warmer and wetter winters without Amazon runoff. Significant warming over the Arctic reduced the local sea-ice extent and enhanced the high latitude river runoff. More importantly, our simulations caution against extreme exploitation of rivers for its far-reaching consequences on climate.

Increased sporadic extremes decrease the intraseasonal variability in the Indian summer monsoon rainfall.

The Indian summer monsoon (ISM) shows quasi-rhythmic intraseasonal oscillations (ISO) manifested as alternate 'active' phases of copious rainfall and quiescent phases of 'break'. Within these periodic phases, the daily rainfall shows large variability and exhibits spatiotemporally sporadic extreme rainfall events. The recent decades have witnessed a significant increase in the number of these extreme rainfall events, especially in the quiescent phases. This increase is accompanied by a decreasing trend in the mean monsoon rainfall and a weakening variance of its low-frequency ISO (LF-ISO) cycle. However, any physical link between this apparent paradox of increased extreme rainfall events and weakened slower-time-scale components is not yet reported. Here, using observations and numerical model simulations, we show that the occurrence of extreme rainfall events, primarily in the break phase of an LF-ISO cycle, reduce the intensity of the following active phase by stabilizing the atmosphere. We found that extreme events in a monsoon break leads to a reduction in the vertical shear of zonal winds and an increase in the static stability of the atmosphere in the following break-to-active transition and active phases. These conditions oppose the initiation and development of an active phase and lessen its intensity. This reduces the LF-ISO intensity and mean ISM rainfall.

An exploration framework to identify and track movement of cloud systems.

We describe a framework to explore and visualize the movement of cloud systems. Using techniques from computational topology and computer vision, our framework allows the user to study this movement at various scales in space and time. Such movements could have large temporal and spatial scales such as the Madden Julian Oscillation (MJO), which has a spatial scale ranging from 1000 km to 10000 km and time of oscillation of around 40 days. Embedded within these larger scale oscillations are a hierarchy of cloud clusters which could have smaller spatial and temporal scales such as the Nakazawa cloud clusters. These smaller cloud clusters, while being part of the equatorial MJO, sometimes move at speeds different from the larger scale and in a direction opposite to that of the MJO envelope. Hitherto, one could only speculate about such movements by selectively analysing data and a priori knowledge of such systems. Our framework automatically delineates such cloud clusters and does not depend on the prior experience of the user to define cloud clusters. Analysis using our framework also shows that most tropical systems such as cyclones also contain multi-scale interactions between clouds and cloud systems. We show the effectiveness of our framework to track organized cloud system during one such rainfall event which happened at Mumbai, India in July 2005 and for cyclone Aila which occurred in Bay of Bengal during May 2009.