Daily PM_2.5 (aerosol particles with an aerodynamic diameter of less than 2.5 μm) samples were collected at an urban site in Chengdu, an inland megacity in southwest China, during four 1-month periods in 2011, with each period in a different season. Samples were subject to chemical analysis for various chemical components ranging from major water-soluble ions, organic carbon (OC), element carbon (EC), trace elements to biomass burning tracers, anhydrosugar levoglucosan (LG), and mannosan (MN). Two models, the ISORROPIA II thermodynamic equilibrium model and the positive matrix factorization (PMF) model, were applied to explore the likely chemical forms of ionic constituents and to apportion sources for PM_2.5. Distinctive seasonal patterns of PM_2.5 and associated main chemical components were identified and could be explained by varying emission sources and meteorological conditions. PM_2.5 showed a typical seasonality of waxing in winter and waning in summer, with an annual mean of 119 μg m⁻³. Mineral soil concentrations increased in spring, whereas biomass burning species elevated in autumn and winter. Six major source factors were identified to have contributed to PM_2.5 using the PMF model. These were secondary inorganic aerosols, coal combustion, biomass burning, iron and steel manufacturing, Mo-related industries, and soil dust, and they contributed 37 ± 18, 20 ± 12, 11 ± 10, 11 ± 9, 11 ± 9, and 10 ± 12%, respectively, to PM_2.5 masses on annual average, while exhibiting large seasonal variability. On annual average, the unknown emission sources that were not identified by the PMF model contributed 1 ± 11% to the measured PM_2.5 mass. Various chemical tracers were used for validating PMF performance. Antimony (Sb) was suggested to be a suitable tracer of coal combustion in Chengdu. Results of LG and MN helped constrain the biomass burning sources, with wood burning dominating in winter and agricultural waste burning dominating in autumn. Excessive Fe (Ex-Fe), defined as the excessive portion in measured Fe that cannot be sustained by mineral dust, is corroborated to be a straightforward useful tracer of iron and steel manufacturing pollution. In Chengdu, Mo / Ni mass ratios were persistently higher than unity, and considerably distinct from those usually observed in ambient airs. V / Ni ratios averaged only 0.7. Results revealed that heavy oil fuel combustion should not be a vital anthropogenic source, and additional anthropogenic sources for Mo are yet to be identified. Overall, the emission sources identified in Chengdu could be dominated by local sources located in the vicinity of Sichuan, a result different from those found in Beijing and Shanghai, wherein cross-boundary transport is significant in contributing pronounced PM_2.5. These results provided implications for PM_2.5 control strategies.

Long-term measurements of aerosol optical depths (AODs) at 440 nm and Ångström exponents (AE) between 440 and 870 nm made for CARSNET were compiled into a climatology of aerosol optical properties for China. Quality-assured monthly mean AODs are presented for 50 sites representing remote, rural, and urban areas. AODs were 0.14, 0.34, 0.42, 0.54, and 0.74 at remote stations, rural/desert regions, the Loess Plateau, central and eastern China, and urban sites, respectively, and the corresponding AE values were 0.97, 0.55, 0.82, 1.19, and 1.05. AODs increased from north to south, with low values (< 0.20) over the Tibetan Plateau and northwestern China and high AODs (> 0.60) in central and eastern China where industrial emissions and anthropogenic activities were likely sources. AODs were 0.20–0.40 in semi-arid and arid regions and some background areas in northern and northeastern China. AEs were > 1.20 over the southern reaches of the Yangtze River and at clean sites in northeastern China. In the northwestern deserts and industrial parts of northeast China, AEs were lower (< 0.80) compared with central and eastern regions. Dust events in spring, hygroscopic particle growth during summer, and biomass burning contribute the high AODs, especially in northern and eastern China. The AODs show decreasing trends from 2006 to 2009 but increased ~ 0.03 per year from 2009 to 2013.

In January 2013, North China Plain experienced several serious haze events. Cimel sunphotometer measurements at seven sites over rural, suburban and urban regions of North China Plain from 1 to 30 January 2013 were used to further our understanding of spatial-temporal variation of aerosol optical parameters and aerosol radiative forcing (ARF). It was found that Aerosol Optical Depth at 500 nm (AOD_{500 nm}) during non-pollution periods at all stations was lower than 0.30 and increased significantly to greater than 1.00 as pollution events developed. The Angstrom exponent (Alpha) was larger than 0.80 for all stations most of the time. AOD_{500 nm} averages increased from north to south during both polluted and non-polluted periods on the three urban sites in Beijing. The fine mode AOD during pollution periods is about a factor of 2.5 times larger than that during the non-pollution period at urban sites but a factor of 5.0 at suburban and rural sites. The fine mode fraction of AOD_{675 nm} was higher than 80% for all sites during January 2013. The absorption AOD_{675 nm} at rural sites was only about 0.01 during pollution periods, while ~0.03–0.07 and 0.01–0.03 during pollution and non-pollution periods at other sites, respectively. Single scattering albedo varied between 0.87 and 0.95 during January 2013 over North China Plain. The size distribution showed an obvious tri-peak pattern during the most serious period. The fine mode effective radius in the pollution period was about 0.01–0.08 μm larger than during non-pollution periods, while the coarse mode radius in pollution periods was about 0.06–0.38 μm less than that during non-pollution periods. The total, fine and coarse mode particle volumes varied by about 0.06–0.34 μm³, 0.03–0.23 μm³, and 0.03–0.10 μm³, respectively, throughout January 2013. During the most intense period (1–16 January), ARF at the surface exceeded −50 W m⁻², −180 W m⁻², and −200 W m⁻² at rural, suburban, and urban sites, respectively. The ARF readings at the top of the atmosphere were approximately −30 W m⁻² in rural and −40–60 W m⁻² in urban areas. Positive ARF at the top of the atmosphere at the Huimin suburban site was found to be different from others as a result of the high surface albedo due to snow cover.

The contribution of meteorology and emissions to long-term PM_2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PM_2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM_2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM_2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PM_2.5: a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM_2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PM_2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM_2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM_2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM_2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM_2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM_2.5 and gas precursors in these years. Although emissions dominated the long-term PM_2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM_2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., −6.3 % to 4.9 % of the annual mean PM_2.5 concentrations in the Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fenwei Plain, −4.8 % to 4.3 % in the Yangtze River Delta, and −25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM_2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future.

Trends in Chinese global radiation, direct horizontal radiation, diffuse radiation, clearness index, diffuse fraction and percentage of possible sunshine duration for the period 1961–2000 were evaluated based on data for daily surface solar radiation and monthly sunshine duration. Annual means for all six variables were calculated for each station and for China as a whole. Linear regression analysis was used to characterize long‐term annual trends in these variables. Over the latter half of the 20th century, there have been significant decreases in global radiation (−4.5 W/m² per decade), direct radiation (−6.6 W/m² per decade), clearness index (−1.1% per decade), and the percentage of possible sunshine duration (−1.28% per decade), but diffuse fraction has increased (1.73% per decade). Although there is some evidence that conditions have improved in the last decade, the consistent spatial and temporal variations of these variables support the theory that increased aerosol loadings were at least partially responsible for the observed decreases in global radiation and direct radiation, the clearness index, and the monthly percentage of possible sunshine duration over much of China.

Aerosol optical depth (AOD) has become a crucial metric for assessing global climate change. Although global and regional AOD trends have been studied extensively, it remains unclear what factors are driving the inter-decadal variations in regional AOD and how to quantify the relative contribution of each dominant factor. This study used a long-term (1980–2016) aerosol dataset from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis, along with two satellite-based AOD datasets (MODIS/Terra and MISR) from 2001 to 2016, to investigate the long-term trends in global and regional aerosol loading. Statistical models based on emission factors and meteorological parameters were developed to identify the main factors driving the inter-decadal changes of regional AOD and to quantify their contribution. Evaluation of the MERRA-2 AOD with the ground-based measurements of AERONET indicated significant spatial agreement on the global scale (r= 0.85, root-mean-square error = 0.12, mean fractional error = 38.7 %, fractional gross error = 9.86 % and index of agreement = 0.94). However, when AOD observations from the China Aerosol Remote Sensing Network (CARSNET) were employed for independent verification, the results showed that MERRA-2 AODs generally underestimated CARSNET AODs in China (relative mean bias = 0.72 and fractional gross error $=-\mathrm{34.3}$ %). In general, MERRA-2 was able to quantitatively reproduce the annual and seasonal AOD trends on both regional and global scales, as observed by MODIS/Terra, although some differences were found when compared to MISR. Over the 37-year period in this study, significant decreasing trends were observed over Europe and the eastern United States. In contrast, eastern China and southern Asia showed AOD increases, but the increasing trend of the former reversed sharply in the most recent decade. The statistical analyses suggested that the meteorological parameters explained a larger proportion of the AOD variability (20.4 %–72.8 %) over almost all regions of interest (ROIs) during 1980–2014 when compared with emission factors (0 %–56 %). Further analysis also showed that SO₂ was the dominant emission factor, explaining 12.7 %–32.6 % of the variation in AOD over anthropogenic-aerosol-dominant regions, while black carbon or organic carbon was the leading factor over the biomass-burning-dominant (BBD) regions, contributing 24.0 %–27.7 % of the variation. Additionally, wind speed was found to be the leading meteorological parameter, explaining 11.8 %–30.3 % of the variance over the mineral-dust-dominant regions, while ambient humidity (including soil moisture and relative humidity) was the top meteorological parameter over the BBD regions, accounting for 11.7 %–35.5 % of the variation. The results of this study indicate that the variation in meteorological parameters is a key factor in determining the inter-decadal change in regional AOD.