Air pollution has been a serious environmental problem in China that damages human health and causes climate change. While air pollution has been extensively investigated, few studies have provided ...systematic research on the recent space-time changes in air pollution components, including the AQI, CO, O3, NO2, SO2, PM10 and PM2.5, over all of China. Based on the national air quality ground observation database, with data from more than 300 cities from May 2014 to December 2018, this study provides a comprehensive analysis of the characteristics and temporal trends of air pollution over the 7 classified regions in China. Compared to 2014, there are significant decreases of air pollutants in 2018, which are 16% AQI, 25% CO, 20% NO2, 52% SO2, 20% PM10, and 28% PM2.5. The constant improvement of air quality is mainly associated with rigorous emission control acts in China, along with the changes of meteorology. In contrast, O3 maximum daily 8 h average (O3MDA8) continuously increased at an average rate of 4.6% per year during the study period. The air pollution components demonstrate distinct differences in spatial distribution, with high values of CO in North China and Northwest China, NO2 in North China and East China, PM10 in Northwest China, PM2.5 in North China and Central China, and SO2 in North China and Northeast China. Generally, air pollution is most serious in the North China Plain and in cities in central and western Xinjiang Province. Causes for these spatial distributions have been discussed from the perspective of emissions.
•87%, 63% and 93% stations showed decreasing CO, NO2 and SO2 in recent 5 years.•78% and 89% stations showed decreasing PM10 and PM2.5 in recent 5 years.•The North China Plain and central-Western Xinjiang areas are the most seriously polluted.•The improvement of air quality is associated with rigorous emission control in China.
From 4 years of observations from Barrow, Alaska, it is shown that the cloud radiative impact on the surface is a net warming effect between October and May and a net cooling in summer. During ...episodes of high surface haze aerosol concentrations and cloudy skies, both the net warming and net cooling are amplified, ranging from +12.2 Wm−2 in February to −11.8 Wm−2 in August. In liquid clouds, approximately 50%–70% of this change is caused by changes in cloud particle effective radius, with the remainder being caused by unknown atmospheric feedbacks that increase cloud water path. While the yearly averaged warming and cooling effects nearly cancel, the timing of the forcing may be a relevant control of the amplitude and timing of sea ice melt.
Key Points
Clouds warm the surface at Barrow, AK, except during the summer
Aerosol haze strengthens cloud warming in winter and cloud cooling in summer
In liquid clouds, 30‐50% of the added surface forcing is due to increased LWP
Though cloud fraction (CF) from Moderate Resolution Imaging Spectroradiometer (MODIS) has been widely used, it remains unclear whether it can fully represent the diurnal variations. This study ...evaluates the time representation (i.e., satellite passes' mean value per day to represent daily average value) error in MODIS CF by using daytime‐only total sky cover and continuous day‐and‐night radar/lidar CF (Active Remote Sensing of Clouds product, ARSCL) from 2000 to 2010 for two Atmospheric Radiation Measurement (ARM) program climate regime sites of Southern Great Plains (SGP) and Manus. By comparing the daily averaged CFs from ARSCL between using all hourly and using the MODIS‐passing‐time observations, it shows a correlation coefficient of 0.93 (0.88) and root mean square deviation (RMSD) of 12.68% (13.27%) over SGP (Manus) site for daily averaged CFs. Differently, it shows a better correlation coefficient of 0.97 (0.97) and smaller RMSD of 2.98% (3.97%) over SGP (Manus) site for monthly averaged CFs. These suggest that considerable errors could be introduced while using the MODIS CF observed at several fixed time points a day to represent average CF at different time scales. Monthly time representation errors have also been evaluated for daytime only and nighttime only, which show even larger values. A further analysis shows that uncertainties caused by the time representation account for about 23% (21%) of the total differences between surface and MODIS CFs over SGP (Manus) site at monthly time scale.
Key Points
Considerable time representation error exists when using cloud fraction productions from MODIS
The time representation errors are ~12.68% (13.27%) and 2.98% (3.97%) at daily and monthly time scale over SGP (Manus) site, respectively
The time representation error accounts for ~23% (21%) of total MODIS CF uncertainties over SGP (Manus) site at monthly time scale
•Notable spatio-temporal patterns of meteorological influences on PM2.5 concentrations.•Comparison of major methods for quantifying PM2.5-meteorology interactions.•Interaction mechanisms between ...PM2.5 concentrations and eight meteorological factors.•Challenges for better understanding meteorological influences on PM2.5 concentrations.•Major meteorological means for reducing PM2.5 concentrations.
Air pollution over China has attracted wide interest from public and academic community. PM2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM2.5 concentrations are essential to understand the variability of PM2.5 and seek methods to control PM2.5. Since 2013, the measurement of PM2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM2.5 concentrations. We start with an introduction of general meteorological conditions and PM2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM2.5 concentrations. Next, major methods used to quantify meteorological influences on PM2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM2.5. The feedback effects of PM2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM2.5 pollution are made finally.
Growth of fine aerosol particles is investigated during the Aerosol-CCN-Cloud Closure Experiment campaign in June 2013 at an urban site near Beijing. Analyses show a high frequency （- 50%） of fine ...aerosol particle growth events, and show that the growth rates range from 2.1 to 6.5 nm h-1 with a mean value of - 5.1 nm h-1. A review of previous studies indicates that at least four mechanisms can affect the growth of fine aerosol particles： vapor condensation, intramodal coagulation, extramodal coagulation, and multi-phase chemical reaction. At the initial stage of fine aerosol particle growth, condensational growth usually plays a major role and coagulation efficiency generally increases with particle sizes. An overview of previous studies shows higher growth rates over megacity, urban and boreal forest regions than over rural and oceanic regions. This is most likely due to the higher condensational vapor, which can cause strong condensational growth of fine aerosol particles. Associated with these multiple factors of influence, there are large uncertainties for the aerosol particle growth rates, even at the same location.
•Global fAOD varied insignificantly over land and ocean during 2008–2017.•A significant decline in fAOD was identified over China.•Land fAOD in winter and land FMF in spring and winter increased ...significantly.•Both fAOD and FMF were significantly associated with O3.
Despite their extremely small size, fine-mode aerosols have significant impacts on the environment, climate, and human health. However, current understandings of global changes in fine-mode aerosols are limited. In this study, we employed newly developed satellite retrieval data and an attentive interpretable deep learning model to explore the status, changes, and association factors of the global fine-mode aerosol optical depth (fAOD) and aerosol fine-mode fraction (FMF) from 2008 to 2017. At the global scale, the results show a significant increasing trend in land FMF (2.34 × 10−3/year); however, the FMF over the ocean and the fAOD over land and ocean did not reveal significant trends. Between 2008 and 2017, high levels of both fAOD (>0.30) and FMF (>0.75) were identified over China, southeastern Asia, India, and Africa. Seasonally, global land FMF showed high values in summer (>0.70) and low values in spring (<0.65), while land fAOD was high in summer (>0.15) but low in winter (<0.13). Importantly, Australia and Mexico experienced significant increasing trends in FMF during all four seasons. At the regional scale, a significant decline in fAOD was identified in China, which indicates that government emission controls and reductions have been effective in recent decades. The deep learning model was used to interpret the result and showed that O3 was significantly associated with changes in both the FMF and fAOD. This finding suggests the importance of synergizing the regulations for both O3 and fine particles. Our work comprehensively examined global spatial and seasonal fAOD and FMF changes and provides a holistic understanding of global anthropogenic impacts.
The Southern Ocean is covered by a large amount of clouds with high cloud albedo. However, as reported by previous climate model intercomparison projects, underestimated cloudiness and overestimated ...absorption of solar radiation (ASR) over the Southern Ocean lead to substantial biases in climate sensitivity. The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models. We employ 10-year satellite observations to evaluate cloud radiative effect (CRE) and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud, radiation, and aerosol. The simulated longwave, shortwave, and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations. Total cloud fraction (CF) is also reasonably simulated in CMIP6, but the comparison of liquid cloud fraction (LCF) reveals marked biases in spatial pattern and seasonal variations. The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macro- and micro-physical properties, including liquid water path (LWP), cloud optical depth (COD), and cloud effective radius, as well as aerosol optical depth (AOD). However, the large underestimation of both LWP and cloud effective radius (regional means ∼20% and 11%, respectively) results in relatively smaller bias in COD, and the impacts of the biases in COD and LCF also cancel out with each other, leaving CRE and ASR reasonably predicted in CMIP6. An error estimation framework is employed, and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE. Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations, while the modeled CRE is too sensitive to LWP and COD. The relationships between cloud effective radius, LWP, and COD are also analyzed to explore the possible uncertainty sources in different models. Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.
Particulate Matter (PM) is an important indicator of the degree of air pollution. The PM type and the ratio of coarse and fine PM particles determine the ability to affect human health and ...atmospheric processes. Using the observation data across the country from 2015 to 2018, this study investigates the distribution and proportion of PM
2.5
and PM
10
at different temporal and spatial scales in mainland China; clarifies the PM
2.5
, PM
10
and PM
2.5
/PM
10
ratios interrelation; and classifies the dust, mixed, and anthropogenic type aerosol. It shows that the annual average concentration of PM
2.5
and PM
10
decreased by 10.55 and 8.78 μg m
−3
in 4 years. PM
2.5
, PM
10
, and PM
2.5
/PM
10
ratios show obvious while different seasonal variations. PM
2.5
is high in winter and low in summer, while PM
10
is high in winter and spring, and low in summer and autumn. Differently, the PM
2.5
/PM
10
ratios are the highest in winter, and the lowest in spring. PM
2.5
/PM
10
ratios show strong independence on PM
2.5
and PM
10
, implying that it can provide extra information about the aerosol pollution such as aerosol type. A classification method about air pollution types is then further proposed based on probability distribution function (PDF) morphology of PM
2.5
/PM
10
ratios. The results show that dust type mainly lies in the west of Hu-Line, mixed type pollution distributes near Hu-Line, and the anthropogenic type dominates over North China Plain and cities in southern China. The results provide insights into China’s future clean air policy making and environmental research.
Carbon monoxide (CO) is an important gas that affects human health and causes air pollution. However, the estimates of CO emissions in China are still subject to large uncertainties. Based on the CO ...mass concentration and the coupled Weather Research and Forecast (WRF) and Stochastic Time-Inverted Lagrangian Transport (STILT) model (WRF-STILT), this study estimates the CO emissions over Zhengzhou, China. The results show that the mean CO mass concentration was 1.17 mg m−3 from November 2017 to February 2018, with a clear diurnal variation. There were two periods of rapidly increasing CO concentration in the diurnal variation, which are 06:00–09:00 and 16:00–20:00 local time. The footprint analysis shows that the observation site is highly influenced by local emissions. The most influential regions to the site observations are northeast and northwest Zhengzhou, which are associated with the geographical barrier of the Taihang Mountains in the north and narrow Fenwei Plain in the west. The inversion result shows that the actual emissions are lower than the inventory estimates. Using the optimal scaling factors, the WRF-STILT simulations of CO concentration agree closely with the CO measurements with the linear fitting regression equation y = 0.87x + 0.15. The slopes of the linear fitting regressions between the WRF-STILT-simulated CO concentrations determined using the optimal emissions and the observations range from 0.72 to 0.89 for four months, and all the fitting results passed the significance test (P < 0.001). These results indicate that the new optimal emissions derived with the scaling factors could better represent the real emission conditions than the a priori emissions if the WRF-STILT model is assumed to be reliable.
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•The observation site is highly influenced by local emissions, followed by regional transport.•The CO mass concentration in Zhengzhou had two fast rise periods in the diurnal variation.•WRF-STILT simulated CO concentrations using the optimal emissions agree well with the observations.•The inversion result shows that the actual emissions are lower than inventory estimates.
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