Improving the understanding between climate variability and observed extremes of global NO₂ over the past 15 years

Abstract

This work addresses the relationship between major dynamical forcings and variability in NO2 column measurements. The dominating impact in Northern Southeast Asia is due to El Niño-Southern Oscillation (ENSO); in Indonesia, Northern Australia and South America is due to Indian Ocean Dipole (IOD); and in Southern China Land and Sea, Populated Northern China, Siberia, Northern and Arctic Eurasia, Central and Southern Africa, and Western US and Canada is due to North Atlantic Oscillation (NAO). That NO2 pollution in Indonesia is modulated by IOD contradicts previous work claiming that the emissions in Indonesia are a function of El Niño impacting upon Aerosol Optical Depth and Fire Radiative Power. Simultaneous impacts of concurrent and lagged forcings are derived using multi-linear regression, demonstrating ENSO impacts future NO2 variability, enhancing NO2 emissions 7–88 weeks in the future, while IOD and NAO in some cases increase the emissions from or the duration of the future burning season as measured by NO2. This impact will also extend to co-emitted aerosols and heat, which may impact the climate. In all cases, lagged forcings exhibit more impact than concurrent forcings, hinting at non-linearity coupling with soil moisture, water table, and other dynamical effects. The regression model formed demonstrates that dynamical forcings are responsible for over 45% of the NO2 emissions variability in most non-urban areas and over 30% in urban China and sub-arctic regions. These results demonstrate the significance of climate forcing on short-lived air pollutants.

Inferring Polluted Asian Absorbing Aerosol Properties Using Decadal Scale AERONET Measurements and a MIE Model

“…This explanation is consistent with highly aged black carbon from far away biomass burning sources know to occur in Sumatra or Borneo (Cohen, 2014) or elsewhere in Southeast Asia (Deng et al, 2020) being transported after undergoing significant aging, but are still significantly more absorbing than the local sources.…”

“…This procedure is repeated three times, merging each iteration of extremes with the previous extremes. This method has been shown to work well for extremes of Ozone Monitoring Instrument NO 2 and Multi‐angle Imaging SpectroRadiometer AOD measurements (Deng et al., 2020).…”

Quantifying the Importance of Ultraviolet Radiation and Non-linear Chemistry on Surface Ozone Prediction

“…Previous studies have pointed out that coal combustion, motor vehicle emissions and industrial sources are major PM 2.5 sources in China, while domestic fuel burning, biomass burning, other anthropogenic emissions sources, as well as dust also contribute to PM 2.5 concentration in China as well (Cohen & Wang, 2014;Huo et al, 2011;Karagulian et al, 2015;Wang, Cohen, et al, 2021;Zhang et al, 2007). PM 2.5 comprises of inorganic sources such as sulfate, nitrate, and mineral dust, and organic sources such as organic carbon and black carbon (BC), with the portion of these components varying in degree based on the day of the year, source time, geographic region, and local meteorology, among other factors (Deng et al, 2021;Ding et al, 2016;Wang, Wang, et al, 2021). PM 2.5 can lead to a decrease in solar radiation reaching at surface through a combination of scattering and absorption, which are in determined by the PM 2.5 concentrations in the air, its chemical composition, its size distribution, its hygroscopic potential and water vapor, its vertical distribution, the land surface properties, meteorology, and more Holben et al, 1998;Huang et al, 2014;Zhang et al, 2015).…”

Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO2 of Mainland China

“…At present, large-scale monitoring research has been realized based on the huge advantages of satellite remote sensing [21], [25]. The retrieval of troposphere NO2 vertical column densities (VCDs) from satellites sensors such as Ozone Monitoring Instrument (OMI) [26], [27] and Global Ozone Monitoring Experiment (GOME) [28]- [30] have been widely used in the mapping of the spatiotemporal distribution and change analysis of tropospheric NO 2 [31]- [34], while higher quality upward looking surface observations such as MAX-DOAS have been used to establish such conditions in specific locations [35], [36]. The relationship between surface and column observations have been constructed to estimate the NS-NO 2 concentration with the support of chemical transport models [37]- [40], geographically and temporally weighted regression [41]- [43], and machine learning methods [13], [20], [32], [35], [44], [45].…”