Recent intensification of winter haze in China linked to foreign emissions and meteorology

Combined application of myo-inositol and corn steep liquor enhances seedling growth and cold tolerance in cucumber and tomato

Low temperatures pose a common challenge in the production of cucumbers and tomatoes, hindering plant growth and, in severe cases, leading to plant death. In our investigation, we observed a substantial improvement in the growth of cucumber and tomato seedlings through the application of corn steep liquor (CSL), myo-inositol (MI), and their combinations. When subjected to low-temperature stress, these treatments resulted in heightened levels of photosynthetic pigments, thereby fostering enhanced photosynthesis in both tomato and cucumber plants. Furthermore, it contributed to a decrease in malondialdehyde (MDA) levels and electrolyte leakage (REP). The effectiveness of the treatment was further validated through the analysis of key gene expressions (CBF1, COR, MIOX4, and MIPS1) in cucumber. Particularly, noteworthy positive outcomes were noted in the treatment involving 0.6 mL L-1 CSL combined with 72 mg L-1 MI. This study provides valuable technical insights into leveraging the synergistic effects of inositol and maize leachate to promote early crop growth and bolster resistance to low temperatures.

Unmasking air quality: A novel image-based approach to align public perception with pollution levels

In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77-0.78, RMSE: 8.31-9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light intensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in stationary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging techniques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception.

The turbulent future brings a breath of fresh air

Ventilation of health hazardous aerosol pollution within the planetary boundary layer (PBL) - the lowest layer of the atmosphere - is dependent upon turbulent mixing, which again is closely linked to the height of the PBL. Here we show that emissions of both CO2 and absorbing aerosols such as black carbon influence the number of severe air pollution episodes through impacts on turbulence and PBL height. While absorbing aerosols cause increased boundary layer stability and reduced turbulence through atmospheric heating, CO2 has the opposite effect over land through surface warming. In future scenarios with increasing CO2 concentrations and reduced aerosol emissions, we find that around 10% of the world's population currently living in regions with high pollution levels are likely to experience a particularly strong increase in turbulence and PBL height, and thus a reduction in intense pollution events. Our results highlight how these boundary layer processes provide an added positive impact of black carbon mitigation to human health.

Abrupt emissions reductions during COVID-19 contributed to record summer rainfall in China

Record rainfall and severe flooding struck eastern China in the summer of 2020. The extreme summer rainfall occurred during the COVID-19 pandemic, which started in China in early 2020 and spread rapidly across the globe. By disrupting human activities, substantial reductions in anthropogenic emissions of greenhouse gases and aerosols might have affected regional precipitation in many ways. Here, we investigate such connections and show that the abrupt emissions reductions during the pandemic strengthened the summer atmospheric convection over eastern China, resulting in a positive sea level pressure anomaly over northwestern Pacific Ocean. The latter enhanced moisture convergence to eastern China and further intensified rainfall in that region. Modeling experiments show that the reduction in aerosols had a stronger impact on precipitation than the decrease of greenhouse gases did. We conclude that through abrupt emissions reductions, the COVID-19 pandemic contributed importantly to the 2020 extreme summer rainfall in eastern China.

GPS-ZTD data assimilation and its impact on wintertime haze prediction over North China Plain using WRF 3DVAR and CMAQ modeling system

Severe haze frequently hits the North China Plain (NCP), especially in winter during recent years. Meteorological factors affect aerosol formation and its optical properties, and accurate meteorological fields are imperative for accurate aerosol simulations. The impacts of Global Positioning System Zenith Total Delay (GPS-ZTD) data assimilation on meteorology and aerosol simulations were evaluated in this study using the WRF-CMAQ (the Weather Research and Forecasting model and Community Multiscale Air Quality) modelling system over the NCP during 01-31 December 2019. After bias correction, GSP-ZTD data were assimilated into the WRF model using the 3DVAR technique. Two sensitivity tests (CTR and ZTD) were conducted. The WRF model had generally acceptable performance for surface and upper air meteorological variables, PM_2.5 and visibility. From the aspect of BIAS, STDE, RMSE, and R, the assimilation of ZTD data improved the underestimation of ground relative humidity (RH). The improvement was more pronounced in the first 18 forecast hours. The mean RH BIAS decreased by 8%. Surface pressure was also improved in ZTD. The influence of ZTD data assimilation on ground temperature and wind tended to be neutral. The BIAS of ZTD decreased by 3% after data assimilation while STED or RMSE increased slightly. After ZTD data assimilation, the PM_2.5 underestimation decreased by 3.4% over NCP. And station mean BIAS or RMSE of PM_2.5 decreased at more than 70% stations. After ZTD data assimilation, the visibility overestimation was reduced by 2.5%. And more than 81% stations over had lower visibility BIAS or RMSE. Station mean PM_2.5 mass concentration increased by 1.5% in ZTD. The primary aerosol species increased by approximately 1%, and most secondary aerosol species increased by greater than 2% affected by both aerosol physical and chemical process. Although the improvement of PM_2.5 seems marginal from the perspective of regional or temporal average, the contribution of ZTD data assimilation on specific pollution episodes at specific stations can be great. The improvement of PM_2.5 troughs was in the range of 1-5 μg/m³, while the overestimation of PM_2.5 peaks was reduced by few up to dozens μg/m³. This will contribute to the extreme value prediction during pollution episode.

Rethinking the causes of extreme heavy winter PM2.5 pollution events in northern China

It has been reported that air quality models largely underestimate PM_2.5 concentrations during severe pollution events in China. In this study, the Models-3 Community Multi-scale Air Quality model (CMAQ) was employed to simulate PM_2.5 concentrations in May-June (non-heating period) and in November-December (heating period) of 2013 in northern China, with a particular focus on determining the causes of the underestimation. Modeling results reproduced the mass concentrations of PM_2.5 in approximately 50% of the non-heating and heating periods in Qingdao (referred to as the good periods), while the model performance was unsatisfactory during the remaining periods (the poor periods). In this respect, the overprediction of inorganic salts and the underprediction of organic matter in PM_2.5 canceled each other out and resulted in a good simulation of PM_2.5 concentrations during the good periods, whereas during poor periods, the bias of the planetary boundary layer height, wind direction, precipitation, and other factors caused inconsistencies between the simulated and observed PM_2.5 concentrations. Sensitivity studies showed that the underestimation of primarily emitted particles from local emissions was likely the main cause of PM_2.5 underpredictions during heavy haze days. Furthermore, our results implied that the assumption of the conditions of the gas-aerosol thermodynamic equilibria in the air quality model likely results in an overprediction of secondary PM_2.5 inorganic salts (SO₄^2- + NO₃^- + NH₄⁺) during clear days. In contrast, during heavy pollution or heavy haze days, high concentrations of air pollutants theoretically rapidly leads to gas/particle chemical equilibrium and no overprediction of SO₄^2-, NO₃^-, and NH₄⁺ concentrations. Nevertheless, the underestimation of primarily emitted particles from local sources during heavy haze days is yet to be explained and needs further investigation.

High-resolution estimation of ambient sulfate concentration over Taiwan Island using a novel ensemble machine-learning model

Heavy loadings of sulfate aerosol trigger haze formation and pose great damage to human health in Taiwan Island. Nevertheless, high-resolution spatiotemporal variation of ambient sulfate across Taiwan Island still remained unknown because of the scarce monitoring sites. Thus, we developed a novel ensemble model named extreme gradient boosting coupled with geographically and temporally weighted regression (XGBoost-GTWR) to predict the high-resolution sulfate concentration (0.05°) based on satellite data, assimilated meteorology, and the output of chemical transport models (CTMs). The result suggested that XGBoost-GTWR model outperformed other five models in predicting the sulfate concentration with the highest R2 value (R2 = 0.58) and the lowest relative mean square error (RMSE = 1.96 μg/m3). Besides, the transferability of the XGBoost-GTWR model was also validated based on the ground-level sulfate data in 2019. The result suggested that the R2 value of the extrapolation equation (0.53) did not show notable decrease compared with the 10-fold cross-validation result (0.58), indicating that the model was robust to predict the sulfate concentration. The ambient sulfate concentration in Taiwan Island displayed featured spatial variation with the highest one in Southwest Taiwan and the lowest one in Northeast Taiwan, respectively. It was assumed that the higher anthropogenic emission combined with the adverse meteorological condition led to the higher sulfate level in the southwestern coastal region. The ambient sulfate concentration exhibited significantly seasonal variation with the highest value in spring (5.65 ± 0.84 μg/m3), followed by those in winter (5.45 ± 1.25 μg/m3) and autumn (4.60 ± 0.80 μg/m3), and the lowest one in summer (3.80 ± 0.65 μg/m3). The higher sulfate concentration in spring was mainly contributed by the dense biomass burning and scarce rainfall amount. The present study develops a novel model to capture the high-resolution sulfate map and provides basic data for effective regulations of air pollution and epidemiological studies.

Mitigated PM2.5 Changes by the Regional Transport During the COVID-19 Lockdown in Shanghai, China

Intensive observations and WRF-Chem simulations are applied in this study to investigate the adverse impacts of regional transport on the PM2.5 (fine particulate matter; diameter ≤2.5 μm) changes in Shanghai during the Coronavirus Disease 2019 lockdown. As the local atmospheric oxidation capacity was observed to be generally weakened, strong regional transport carried by the frequent westerly winds is suggested to be the main driver of the unexpected pollution episodes, increasing the input of both primary and secondary aerosols. Contributing 40%-80% to the PM2.5, the transport contributed aerosols are simulated to exhibit less decreases (13.2%-21.8%) than the local particles (37.1%-64.8%) in urban Shanghai due to the lockdown, which largely results from the less decreased industrial and residential emissions in surrounding provinces. To reduce the influence of the transport, synergetic emission control, especially synergetic ammonia control, measures are proved to be effective strategies, which need to be considered in future regulations.

Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980-2019 using a machine learning approach

The lack of long-term observations and satellite retrievals of health-damaging fine particulate matter in China has demanded the estimates of historical PM_2.5 (particulate matter less than 2.5 μm in diameter) concentrations. This study constructs a gridded near-surface PM_2.5 concentration dataset across China covering 1980-2019 using the space-time random forest model with atmospheric visibility observations and other auxiliary data. The modeled daily PM_2.5 concentrations are in excellent agreement with ground measurements, with a coefficient of determination of 0.95 and mean relative error of 12%. Besides the atmospheric visibility which explains 30% of total importance of variables in the model, emissions and meteorological conditions are also key factors affecting PM_2.5 predictions. From 1980 to 2014, the model-predicted PM_2.5 concentrations increased constantly with the maximum growth rate of 5-10 μg/m³/decade over eastern China. Due to the clean air actions, PM_2.5 concentrations have decreased effectively at a rate over 50 μg/m³/decade in the North China Plain and 20-50 μg/m³/decade over many regions of China during 2014-2019. The newly generated dataset of 1-degree gridded PM_2.5 concentrations for the past 40 years across China provides a useful means for investigating interannual and decadal environmental and climate impacts related to aerosols.

Interannual variability and trends of combustion aerosol and dust in major continental outflows revealed by MODIS retrievals and CAM5 simulations during 2003-2017

Emissions and long-range transport of mineral dust and combustion-related aerosol from burning fossil fuels and biomass vary from year to year, driven by the evolution of the economy and changes in meteorological conditions and environmental regulations. This study offers both satellite and model perspectives on the interannual variability and possible trends of combustion aerosol and dust in major continental outflow regions over the past 15 years (2003-2017). The decade-long record of aerosol optical depth (AOD, denoted as τ), separately for combustion aerosol (τ _c) and dust (τ _d), over global oceans is derived from the Collection 6 aerosol products of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard both Terra and Aqua. These MODIS Aqua datasets, complemented by aerosol source-tagged simulations using the Community Atmospheric Model version 5 (CAM5), are then analyzed to understand the interannual variability and potential trends of τ _c and τ _d in the major continental outflows. Both MODIS and CAM5 consistently yield a similar decreasing trend of -0.017 to -0.020 per decade for τ _c over the North Atlantic Ocean and the Mediterranean Sea that is attributable to reduced emissions from North America and Europe, respectively. On the contrary, both MODIS and CAM5 display an increasing trend of +0.017 to +0.036 per decade for τ _c over the tropical Indian Ocean, the Bay of Bengal, and the Arabian Sea, which reflects the influence of increased anthropogenic emissions from South Asia and the Middle East in the last 2 decades. Over the northwestern Pacific Ocean, which is often affected by East Asian emissions of pollution and dust, the MODIS retrievals show a decreasing trend of -0.021 per decade for τ _c and -0.012 per decade for τ _d, which is, however, not reproduced by the CAM5 model. In other outflow regions strongly influenced by biomass burning smoke or dust, both MODIS retrievals and CAM5 simulations show no statistically significant trends; the MODIS-observed interannual variability is usually larger than that of the CAM5 simulation.

Unraveling driving forces explaining significant reduction in satellite-inferred Arctic surface albedo since the 1980s

The Arctic has warmed significantly since the early 1980s and much of this warming can be attributed to the surface albedo feedback. In this study, satellite observations reveal a 1.25 to 1.51% per decade absolute reduction in the Arctic mean surface albedo in spring and summer during 1982 to 2014. Results from a global model and reanalysis data are used to unravel the causes of this albedo reduction. We find that reductions of terrestrial snow cover, snow cover fraction over sea ice, and sea ice extent appear to contribute equally to the Arctic albedo decline. We show that the decrease in snow cover fraction is primarily driven by the increase in surface air temperature, followed by declining snowfall. Although the total precipitation has increased as the Arctic warms, Arctic snowfall is reduced substantially in all analyzed data sets. Light-absorbing soot in snow has been decreasing in past decades over the Arctic, indicating that soot heating has not been the driver of changes in the Arctic snow cover, ice cover, and surface albedo since the 1980s.

Fine particles cause the abnormality of cardiac ATP levels via PPARɑ-mediated utilization of fatty acid and glucose using in vivo and in vitro models

Ambient fine particle (PM_2.5) is one of the potential risk factors for the cardiovascular disease, which is characterized by a marked shift in energy substrate preference leading to the reduction of adenosine triphosphate (ATP) synthesis. The metabolic adaptation is brought about by alterations in substrate transporters. Hence, this study aimed to investigate the effects and possible mechanisms of seasonal PM_2.5 exposure on alteration of cardiac ATP content. Sprague Dawley (SD) rats were exposed to summer and winter PM_2.5 for two months to generate a cardiac damage phenotype, characterized by apoptosis, lipid peroxidation, and ATP depletion. Reduced fatty acid content and elevated glucose content were observed in haze dose PM_2.5-exposed SD rats and rat cardiomyocyte cells. Expressions of their transporters in PM_2.5-treated groups exhibited the homologous trends. Moreover, PM_2.5 exposure repressed the expression and translocation of peroxisome proliferator-activated receptor alpha (PPARα) in a dose-dependent manner. However, the addition of WY-14643 (an inhibitor of PPARα) prominently alleviated the above phenomenons. The effect of PM_2.5 in winter was found to be more serious than in summer. These results demonstrated that seasonal PM_2.5 exposure causes the abnormality of cardiac ATP generation through the regulation of PPARα-mediated selection and utilization of energy substrates and their transporters. This study contributes in better understanding of haze-induced cardiovascular disease by revealing crucial indicators involved in this phenomenon.

Sensitivity of projected PM2.5- and O3-related health impacts to model inputs: A case study in mainland China

In China, fine particulate matter (PM_2.5) and ground-level ozone (O₃) are anticipated to continuously affect large populations in the coming decades. Simulations of the levels of these pollutants largely depend on emissions inputs, which are highly uncertain both in magnitude and spatial distribution. Our goal was to explore sensitivities of projected changes in PM_2.5- and O₃-related short-term health impacts in mainland China to emissions and other model inputs. We simulated winter PM_2.5 and summer O₃ concentrations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for both 2008 and 2050. We used three emission inventories in 2008 and four emissions scenarios in 2050. The resulting air pollutant concentrations were combined with eight population projections and three concentration-response functions (CRFs) to estimate future PM_2.5- and O₃-related health impacts including total, cardiovascular, and respiratory mortalities in mainland China. Multivariate analysis of variance was used to apportion the uncertainty due to different model parameters. Combinations of different parameters produced a wide range of national PM_2.5- and O₃-related mortalities. CRFs and present emissions each contribute 38%-56% and 20%-28% of the total sum of squares for PM_2.5-related mortalities. Future emissions are the largest source of uncertainty in O₃-related mortality estimates, contributing 24%-48% of total sum of squares. Our results suggest that conducting more epidemiological studies and constraining the present day emissions are essential for projecting future air pollutant-related health impacts in mainland China.

Differences in Sulfate Aerosol Radiative Forcing between the Daytime and Nighttime over East Asia Using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) Model

The effect of aerosols is an important indicator of climate change. Sulfate aerosols, as the major scattering aerosols, which have attracted more and more attention in recent years. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) were utilized to investigate the spatial distribution of sulfate aerosols and their radiative forcing characteristics over East Asia in 2010. Results showed that sulfate aerosols were mainly distributed over eastern China (24–43° N, 101–126° E), especially in the Sichuan Basin. The concentration of sulfate aerosols decreased with increasing altitude over East Asia. It also exhibited obvious seasonal variations, where the largest range of sulfate aerosol concentrations was found in summer, with a maximum of 2.4 μg kg−1 over eastern China. Although sulfate aerosol concentrations varied slightly during day and night, there was still a significantly difference in the sulfate aerosol radiative forcing. Specifically, the magnitude of the direct radiative forcing induced by sulfate aerosols at the surface was approximately −3.02 W m−2 in the daytime, while that was +0.24 W m−2 in the nighttime. This asymmetric change that was caused by the radiative forcing of sulfate aerosols between day and night would have significant impacts on climate change at the regional scale.

Variations of Haze Pollution in China Modulated by Thermal Forcing of the Western Pacific Warm Pool

In addition to the impact of pollutant emissions, haze pollution is connected with meteorology and climate change. Based on the interannual change analyses of meteorological and environmental observation data from 1981 to 2010, we studied the relationship between the winter haze frequency in central-eastern China (CEC) and the interannual variations of sea surface temperature (SST) over Western Pacific Warm Pool (WPWP) and its underlying mechanism to explore the thermal effect of WPWP on haze pollution variation in China. The results show a significant positive correlation coefficient reaching up to 0.61 between the interannual variations of SST in WPWP and haze pollution frequency in the CEC region over 1981–2010, reflecting the WPWP’s thermal forcing exerting an important impact on haze variation in China. The anomalies of thermal forcing of WPWP could induce to the changes of East Asian winter monsoonal winds and the vertical thermal structures in the troposphere over the CEC region. In the winter with anomalously warm SST over the WPWP, the near-surface winds were declined, and vertical thermal structure in the lower troposphere tended to be stable over the CEC-region, which could be conducive to air pollutant accumulation leading to the more frequent haze occurrences especially the heavy haze regions of Yangtze River Delta (YRD) and Pearl River Delta (PRD); In the winter with the anomalously cold WPWP, it is only the reverse of warm WPWP with the stronger East Asian winter monsoonal winds and the unstable thermal structure in the lower troposphere, which could attribute to the less frequent haze pollution over the CEC region. Our study revealed that the thermal forcing of the WPWP could have a modulation on air environment change in China.

Origin and Radiative Forcing of Black Carbon Aerosol: Production and Consumption Perspectives

Air pollution, a threat to air quality and human health, has attracted ever-increasing attention in recent years. In addition to having local influence, air pollutants can also travel the globe via atmospheric circulation and international trade. Black carbon (BC), emitted from incomplete combustion, is a unique but representative particulate pollutant. This study tracked down the BC aerosol and its direct radiative forcing to the emission sources and final consumers using the global chemical transport model (MOZART-4), the rapid radiative transfer model for general circulation simulations (RRTM), and a multiregional input-output analysis (MRIO). BC was physically transported (i.e., atmospheric transport) from western to eastern countries in the midlatitude westerlies, but its magnitude is near an order of magnitude higher if the virtual flow embodied in international trade is considered. The transboundary effects on East and South Asia by other regions increased from about 3% (physical transport only) to 10% when considering both physical and virtual transport. The influence efficiency on East Asia was also large because of the comparatively large emission intensity and emission-intensive exports (e.g., machinery and equipment). The radiative forcing in Africa imposed by consumption from Europe, North America, and East Asia (0.01 Wm^-2) was even larger than the total forcing in North America. Understanding the supply chain and incorporating both atmospheric and virtual transport may improve multilateral cooperation on air pollutant mitigation both domestically and internationally.