Exploring the relationship between surface PM<sub>2.5</sub> and meteorology in Northern India

Abstract

Abstract. Northern India (23)(24)(25)(26)(27)(28)(29)(30)(31) • N, 68-90 • E) is one of the most densely populated and polluted regions in world. Accurately modeling pollution in the region is difficult due to the extreme conditions with respect to emissions, meteorology, and topography, but it is paramount in order to understand how future changes in emissions and climate may alter the region's pollution regime. We evaluate the ability of a developmental version of the new-generation NOAA GFDL Atmospheric Model, version 4 (AM4) to simulate observed wintertime fine particulate matter (PM 2.5 ) and its relationship to meteorology over Northern India. We compare two simulations of GFDL-AM4 nudged to observed meteorology for the period 1980-2016 driven by pollutant emissions from two global inventories developed in support of the Coupled Model Intercomparison Project Phases 5 (CMIP5) and 6 (CMIP6), and compare results with ground-based observations from India's Central Pollution Control Board (CPCB) for the period 1 October 2015-31 March 2016. Overall, our results indicate that the simulation with CMIP6 emissions produces improved concentrations of pollutants over the region relative to the CMIP5-driven simulation.While the particulate concentrations simulated by AM4 are biased low overall, the model generally simulates the magnitude and daily variability of observed total PM 2.5 . Nitrate and organic matter are the primary components of PM 2.5 over Northern India in the model. On the basis of correlations of the individual model components with total observed PM 2.5 and correlations between the two simulations, meteorology is the primary driver of daily variability. The model correctly reproduces the shape and magnitude of the seasonal cycle of PM 2.5 , but the simulated diurnal cycle misses the early evening rise and secondary maximum found in the observations. Observed PM 2.5 abundances are by far the highest within the densely populated IndoGangetic Plain, where they are closely related to boundary layer meteorology, specifically relative humidity, wind speed, boundary layer height, and inversion strength. The GFDL AM4 model reproduces the overall observed pollution gradient over Northern India as well as the strength of the meteorology-PM 2.5 relationship in most locations.

On the widespread enhancement in fine particulate matter across the Indo-Gangetic Plain towards winter

“…Numerous efforts based on in-situ and satellite-based observations have revealed strong enhancements in the PM 2.5 over this region towards winter, typically every year [13][14][15][16] . PM 2.5 concentrations over the IGP region are observed to exceed the standards of World Health Organization (WHO) as well as the National Ambient Air Quality Standards (NAAQS) of India, attributed to the combined effects from a variety of anthropogenic and biomass-burning emissions and meteorological conditions [17][18][19][20][21][22][23][24] . The accumulation of aerosols in this region is further supported by the topography of Himalaya that extends from northwest to southeast and forms a corridor along the IGP.…”

“…The changes in meteorology and atmospheric dynamics can offset the benefits availed from reduction in anthropogenic emissions. Such meteorological influences, besides the elevated emission source strengths and upwind biomass-burning, highly complicate the detailed understanding of aerosol behavior over the IGP 18,[22][23][24] . The implementation of an odd-even scheme for vehicular traffic aiming to reduce its emissions in Delhi, India's capital territory, did not lead to a significant improvement in the air quality 25,26 , which highlighs the crucial need of studies on the spatio-temporal distribution of PM 2.5 over the complex environment of IGP region.…”

Rooney et al 2019, author responses to reviewer comments

“…In this region, the major sources of emissions of primary PM 2.5 and of precursors to secondary PM 2.5 are coal-fired power plants, industries, agricultural biomass burning, transportation, and combustion of biomass fuels for heating and cooking (Reddy and Venkataraman, 2002;Rehman et al, 2011). The southwest monsoon in summer months in India leads to lower pollution levels than in winter months, which are characterized by low wind speeds, shallow boundary layer depths, and high relative humidity (Sen et al, 2017). With the difficulty in determining representative emissions estimates (Jena et al, 2015;Zhong et al, 2016), simulating the extremely high PM 2.5 observations in the Indo-Gangetic Plain has remained a challenge (Schnell et al, 2018).…”

“…The southwest monsoon in summer months in India leads to lower pollution levels than in winter months, which are characterized by low wind speeds, shallow boundary layer depths, and high relative humidity (Sen et al, 2017). With the difficulty in determining representative emissions estimates (Jena et al, 2015;Zhong et al, 2016), simulating the extremely high PM 2.5 observations in the Indo-Gangetic Plain has remained a challenge (Schnell et al, 2018).…”

“…The rainy season, July through September, is characterized by average temperatures around 30 • C and primarily easterly and southeasterly winds. In a study related to the present one, Schnell et al (2018) used emission datasets developed for the Coupled Model Intercomparison Project Phases 5 (CMIP5) and 6 (CMIP6) to evaluate the impact on predicted PM 2.5 over northern India, October-March 2015-2016, with special attention paid to the effect of meteorology of the region, including relative humidity, boundary layer depth, strength of the temperature inversion, and low-level wind speed. In that work, nitrate and organic matter (OM) were predicted to be the dominant components of total PM 2.5 over most of northern India.…”