Seasonal Transition in PM₁₀ Exposure and Associated All-Cause Mortality Risks in India

Abstract

Lack of a consistent PM (particulate matter smaller than 10 μm) database at high spatial resolution hinders in assessing the environmental impact of PM in India. Here we propose an alternate approach to estimate the PM database. Aerosol extinction coefficients at the surface are calculated from midvisible aerosol optical depth from MERRA-2 reanalysis data using characteristics vertical profiles from CALIOP and then are converted to PM mass using aerosol property information and microphysical data. The retrieved PM are bias-corrected and evaluated ( R = 0.85) against coincident ground-based data maintained under the Central Pollution Control Board network. PM exposure exceeds Indian annual air quality standard in 72.3% districts. Transition in PM exposure from the monsoon (Jun-Sep) to postmonsoon season (Oct-Nov) translates to 1-2% higher all-cause mortality risk over the polluted Indo-Gangetic Basin (IGB). Mortality risk increases in the central to eastern IGB and central India and reduces in Delhi national capital region in the winter (Dec-Feb) relative to the postmonsoon season. Mortality risk decreases by 0.5-1.8% in most parts of India in the premonsoon season (Mar-May). Our results quantify the vulnerability in terms of seasonal transition in all-cause mortality risks due to PM exposure at district level for the first time in India.

A Satellite-Based High-Resolution (1-km) Ambient PM2.5 Database for India over Two Decades (2000–2019): Applications for Air Quality Management

“…In India, the epidemiological studies are either time-series (as summarized in [12]) or by design establishing the association, not causality [45], or the acute exposure impact on health outcomes like birthweight [46]. For the chronic exposure impacts on mortality and various health outcomes, we still rely on the GBD framework [1,2,4] that does not include any cohort study from India on ambient PM 2.5 exposure.…”

“…PM 2.5 is the highest in the winter season across the country except for the high-altitude regions in the north and the western arid region. This wintertime enhancement (a positive anomaly ranging from 5 to 40 µg/m 3 relative to the annual average) in PM 2.5 has been attributed to the additional emission from households (especially in the colder places due to space and water heating) and a stable atmosphere under calm conditions [12]. In the western arid region, dust activity remains at a minimum during the winter and in the high-altitude states of Jammu and Kashmir, Himachal Pradesh and Uttarakhand, major commercial activities remain closed due to extreme cold.…”

Characterization of Ice‐Nucleating Particles Over Northern India

“…Results from this study are broadly consistent with this trend. Rapid urbanization with changing land use patterns has led to PM 10 exceeding 250 μg/m 3 over Northern India (Pande et al, ). If pollution were a strong source of INP, one would expect INP concentration exceeding those from the range of INP compiled from all regions of the globe that are not as much affected by pollution.…”

“…A small change in INP concentration can significantly modulate indirect ice effects on climate and precipitation (DeMott et al, ; Storelvmo et al, ; Yun & Penner, ). The Indian subcontinent has elevated aerosol concentrations (e.g., PM 10 concentrations of >250 μg/m 3 in the Indo‐Gangetic Basin) relative to typical continental conditions due to anthropogenic emissions (Pande et al, ). Padmakumari et al () observed high aerosol loading (above 5,000 particles cm −3 ) with hazy conditions and cloud ice above 6 km with temperatures below −14 °C.…”

Analysis of Air Pollution Data in India between 2015 and 2019

“…Recently, extreme levels of fine particulate air pollution in India, combined with a growing appreciation of the adverse impacts of elevated air pollution on health, led the Indian Most research studies analyzing ground monitoring data have focused on Delhi and the surrounding National Capital Region (NCR) (Guttikunda and Gurjar, 2012;Sahu and Kota, 2017;Sharma et al, 2018;Chowdhury et al, 2019;Guttikunda et al, 2019;Wang and Chen, 2019;Hama et al, 2020), and other major cities (Gurjar et al, 2016;Sreekanth et al, 2018, Yang et al, 2018Chen et al, 2020). In addition, some studies also used ground observations to bias correct satellite measurements for India (Pande et al, 2018;Chowdhury et al, 2019;Navinya et al, 2020). However, a need remains for a comprehensive analysis of all surface data collected by manual NAMP and continuous CAAQM monitoring networks between 2015-2019 over which period monitoring increased substantially.…”