Assessment of MERRA-2 Surface PM2.5 over the Yangtze River Basin: Ground-based Verification, Spatiotemporal Distribution and Meteorological Dependence

Abstract

A good understanding of how meteorological conditions exacerbate or mitigate air pollution is critical for developing robust emission reduction policies. Thus, based on a multiple linear regression (MLR) model in this study, the quantified impacts of six meteorological variables on PM2.5 (i.e., particle matter with diameter of 2.5 µm or less) and its major components were estimated over the Yangtze River Basin (YRB). The 38-year (1980–2017) daily PM2.5 and meteorological data were derived from the newly-released Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2) products. The MERRA-2 PM2.5 was underestimated compared with ground measurements, partly due to the bias in the MERRA-2 Aerosol Optical Depth (AOD) assimilation. An over-increasing trend in each PM2.5 component occurred for the whole study period; however, this has been curbed since 2007. The MLR model suggested that meteorological variability could explain up to 67% of the PM2.5 changes. PM2.5 was robustly anti-correlated with surface wind speed, precipitation and boundary layer height (BLH), but was positively correlated with temperature throughout the YRB. The relationship of relative humidity (RH) and total cloud cover with PM2.5 showed regional dependencies, with negative correlation in the Yangtze River Delta (YRD) and positive correlation in the other areas. In particular, PM2.5 was most sensitive to surface wind speed, and the sensitivity was approximately −2.42 µg m−3 m−1 s. This study highlighted the impact of meteorological conditions on PM2.5 growth, although it was much smaller than the anthropogenic emissions impact.

Improving regional air quality predictions in the Indo-Gangetic Plain-Case study of an intensive pollution episode in November 2017

“…Moreover, the model underestimated aerosol loadings over central India. Other studies have reported biased low AOD and corresponding PM2.5 concentrations over other polluted regions (He et al, 2019;Song et al, 2018). Nov. 24 th , on the other hand, represented a day with no significant fire emission.…”

“…The MERRA-2 PM2.5 was based on the mass mixing ratios of black carbon, organic carbon, dust, sea-salt, and sulfate. Since ammonium concentration is not available, it is common in the literature to assume that sulfate ion will 210 be completely neutralized by ammonium and form ammonium sulfate and therefore a factor of 1.375 was assumed in calculating inorganic aerosol concentrations (Buchard et al, 2016;He et al, 2019;Provençal et al, 2017). On the other hand, literature suggest organic carbon concentration should be multiplied by 1.4 to compensate for other missing organic compounds to estimate the organic mass (Buchard et al, 2016;Chow et al, 2015;He et al, 2019;Provençal et al, 2017;Turpin and Lim, 2001).…”

“…b) location of CPCB stations (black stars) and US Embassy station (red star), c) Calendar map of averaged daily PM2.5 concentration measured at US Embassy, d) Calendar map of averaged daily ozone concentration measured 75 at North Campus, DU Models usually underestimate the concentrations during extreme pollution periods unless they apply chemical data assimilation (Dekker et al, 2019;Kulkarni et al, 2020;Kumar et al, 2015b). Moreover, there are different input data in terms of chemical boundary conditions and fire emissions that can affect air quality modeling results (He et al, 2019).The main objective of this study is to investigate the impacts of different global input data sets on improving modeled PM2.5 concentrations over the IGP 80 during Nov. 2017. We hypothesize that incorporating different available datasets with each other can improve modeling results.…”

A homogenized daily <i>in situ</i> PM<sub>2.5</sub> concentration dataset from national air quality monitoring network in China

“…Open Access that the PM2.5 time series estimated from the MERRA-2 aerosol reanalysis in the same way as described in He et al (2019) was used. The one more correlated to this external PM2.5 time series was then used as the reference (deemed as homogeneous) while the other was considered as the base series (i.e., implies to be adjusted).…”

Relationship between Air Pollution and Urban Forms: Evidence from Prefecture-Level Cities of the Yangtze River Basin

“…Air quality is influenced by a variety of meteorological and socioeconomic factors [4,5,6,7,8,9,10]. However, a number of empirical studies, primarily in developed countries, demonstrated that urban form, such as size, shape, population density, compactness, and fragmentation has an important effect on local air pollution [11,12,13,14,15].…”