Mitigation of PM<sub>2.5</sub> and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period

Abstract

Abstract. Fine particulate matter (PM2.5) and surface ozone (O3) are major air pollutants in megacities such as Delhi, but the design of suitable mitigation strategies is challenging. Some strategies for reducing PM2.5 may have the notable side effect of increasing O3. Here, we demonstrate a numerical framework for investigating the impacts of mitigation strategies on both PM2.5 and O3 in Delhi. We use Gaussian process emulation to generate a computationally efficient surrogate for a regional air quality model (WRF-Chem). This allows us to perform global sensitivity analysis to identify the major sources of air pollution and to generate emission-sector-based pollutant response surfaces to inform mitigation policy development. Based on more than 100 000 emulation runs during the pre-monsoon period (peak O3 season), our global sensitivity analysis shows that local traffic emissions from the Delhi city region and regional transport of pollution emitted from the National Capital Region (NCR) surrounding Delhi are dominant factors influencing PM2.5 and O3 in Delhi. They together govern the O3 peak and PM2.5 concentration during daytime. Regional transport contributes about 80% of the PM2.5 variation during the night. Reducing traffic emissions in Delhi alone (e.g. by 50 %) would reduce PM2.5 by 15 %–20 % but lead to a 20 %–25 % increase in O3. However, we show that reducing NCR regional emissions by 25 %–30 % at the same time would further reduce PM2.5 by 5 %–10 % in Delhi and avoid the O3 increase. This study provides scientific evidence to support the need for joint coordination of controls on local and regional scales to achieve effective reduction in PM2.5 whilst minimising the risk of O3 increase in Delhi.

Investigating the background and local contribution of the oxidants in London and Bangkok

“…40 The anthropogenic emissions for the Bangkok study were extracted from the global Emission Database for Global Atmospheric Research with 41,42 These datasets are provided as monthly means giving the seasonal cycles. Daily activity cycles, and speciation of NMVOC emissions (see Supplementary Information, Table S1), have been imposed (see details in Chen et al 43 and Lowe). 44 The 25 study are caused by the fact that the Bangkok data set is a much longer data set, and the initial years of the study (2005)(2006)(2007)(2008)(2009) resulted in much higher local pollution levels as emissions control technologies or vehicle fleet composition were not implemented during this time period.…”

Evaluating the sensitivity of fine particulate matter (PM<sub>2.5</sub>) simulations to chemical mechanism in Delhi

“…In this study, we used the Weather Research and Forecasting model coupled with chemistry WRF-Chem v3.9.1 to simulate surface PM 2.5 mass concentration during the peak winter period, starting from 1 December 2017 to 31 January 2018. Recently, the model has been widely used to simulate the air quality in Delhi (Beig et al, 2013;Gupta and Mohan 2015;Kulkarni et al, 2020;Chen et al, 2020) and to estimate NO X and PM 2.5 mass concentration over India (Ghude et al, 2013;Krishana et al, 2019;Ojha et al, 2020;Beig et al, 2020). In this study, three sets of simulations were designed using following three widely used coupled schemes (gas-phase chemical mechanisms with aerosol schemes) to simulate the PM 2.5 mass concentrations over the northern region of India.…”

“…Diversity of emission sources (Chandra et al, 2018;Hakkim et al, 2019), larger use of fossil fuel such as transport, industries, etc. (Chen et al, 2020), and…”

“…This has drawn significant academic and research interest in predicting high PM 2.5 levels using numerical air quality models (Guttikunda et al, 2012;Beig et al, 2013;Krishna et al, 2019;Kulkarni et al, 2020). Few recent studies have tested the performance of air quality models, particularly WRF-Chem, in simulating hourly PM 2.5 concentrations in Delhi (Ojha et al,2020;Chen et al, 2020;Kulkarni et al, 2020). These studies suggested that simulating and predicting extreme air quality episodes, particularly PM 2.5 concentrations exceeding 300µg/m 3 , in the NCR region is a challenging task for the air quality models (Kumar et al, 2015;Krishna et al, 2019;Bali et al, 2019).…”

Temporally-resolved sectoral and regional contributions to air pollution in Beijing: Informing short-term emission controls

“…We therefore apply a computationally fast surrogate model to fully explore these relationships in a computationally tractable manner. We choose a Gaussian Process based statistical emulator as a surrogate model as it requires relatively few training runs and is well-suited to mapping the non-linear but continuous relationship between emissions and concentrations characteristic of PM 2.5 (see Ryan et al, 2018;Chen et al, 2020). We focus here on 12 emission sources that include the four key sectors in each of the three regions considered here.…”