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This paper presents a new retrieval method for inferring the vertical profile of cirrus cloud effective particle size by using solar reflected line spectra in the 1.38‐μm band. The retrieval method is based on the maximum‐photon penetration principle coupled with the constrained linear inversion. This approach takes advantage of the vertical stratification of cirrus cloud effective particle size as well as absorption lines of water vapor of different intensity, which contain rich information on the vertical structure of cloud particle size. Reflected radiances at different wavenumbers provide the effective‐size information at different heights within cirrus associated with photon different penetration depths. Assuming a vertical profile of effective size monotonically decreasing toward cloud top and using results based on “exact” radiative transfer computations, we perform retrieval of the effective size for a number of model cirrus to check for algorithm accuracy. The retrieved profile of effective size is close to the model profile for cirrus optical depth less than about eight with an uncertainty range of 2.2–4.2 μm. In addition, we further carry out a sensitivity study involving the retrieved effective size in connection with different water vapor profiles and demonstrate that the difference from the model is only several percent except for the cloud top if an appropriate wavenumber set is selected. The results from this study suggest that the present method can be applied to realistic remote sensing of the vertical profile of cirrus cloud particle size.

The frequent episodes of severe air pollution over China during recent years have posed serious health threats to densely populated eastern China. Although several studies investigated the linkage between enhanced severity and frequency of air pollution and the large‐scale weather patterns over China, the day‐to‐day covariability between them, as well as its local and remote mechanisms, has not been systematically documented. The wintertime synoptic covariability between PM_2.5and large‐scale meteorological fields is studied using surface observations of PM_2.5in 2013/2014–2016/2017 and ERA‐Interim meteorological fields through maximum covariance analysis (MCA). The first MCA mode (MCA1) suggests a consistent accumulation of ambient PM_2.5as a result of weakened winds that block the pollutant removal passage in heavily polluted areas of eastern China, as well as moist air from southeast coast favoring haze formation. A northeast–southwest belt that extends into northeastern China and central China on each end is more sensitive to MCA1. The second MCA mode (MCA2) shows a north–south dipole in PM_2.5linked to the contrast of boundary layer height and surface wind speed between northern and southern regions of China. Spatial patterns of both modes are supported by the GEOS‐Chem chemistry transport model with realistic emission inventory. The spatial patterns of the two modes are robust on the interannual time scales. Based on that, we investigate the variability of the first two modes of the identified modes on the multidecadal scale by projecting GPM_500 pattern to 1981–2010. Correlation analysis of the projected time series and climate indices over 30 years indicates the possible linkage of Arctic oscillation, ENSO indices, Pacific decadal oscillation and east Atlantic/western Russia to regional air pollution patterns over China.

To tackle the severe fine particle (PM_2.5) pollution in China, the government has implemented stringent control policies mainly on power plants, industry, and transportation since 2005, but estimates of the effectiveness of the policy and the temporal trends in health impacts are subject to large uncertainties. By adopting an integrated approach that combines chemical transport simulation, ambient/household exposure evaluation, and health-impact assessment, we find that the integrated population-weighted exposure to PM_2.5(IPWE) decreased by 47% (95% confidence interval, 37–55%) from 2005 [180 (146–219) μg/m³] to 2015 [96 (83–111) μg/m³]. Unexpectedly, 90% (86–93%) of such reduction is attributed to reduced household solid-fuel use, primarily resulting from rapid urbanization and improved incomes rather than specific control policies. The IPWE due to household fuels for both cooking and heating decreased, but the impact of cooking is significantly larger. The reduced household-related IPWE is estimated to avoid 0.40 (0.25–0.57) million premature deaths annually, accounting for 33% of the PM_2.5-induced mortality in 2015. The IPWE would be further reduced by 63% (57–68%) if the remaining household solid fuels were replaced by clean fuels, which would avoid an additional 0.51 (0.40–0.64) million premature deaths. Such a transition to clean fuels, especially for heating, requires technology innovation and policy support to overcome the barriers of high cost of distribution systems, as is recently being attempted in the Beijing–Tianjin–Hebei area. We suggest that household-fuel use be more highly prioritized in national control policies, considering its effects on PM_2.5exposures.