Observational evidence and mechanisms of aerosol effects on precipitation.

Aerosols greatly influence precipitation characteristics, thereby impacting the regional climate and human life. As an indispensable factor for cloud formation and a critical radiation budget regulator, aerosols can affect precipitation intensity, frequency, geographical distribution, area, and time. However, discrepancies exist among current studies due to aerosol properties, precipitation types, the vertical location of aerosols and meteorological conditions. The development of technology has driven advances in current research, but understanding the aerosol effects on precipitation remain complex and challenging. This paper revolves around the following topics from the two perspectives of Aerosol-Radiation Interaction (ARI) and Aerosol-Cloud Interaction (ACI): (1) the influence of different vertical locations of absorbing/scattering aerosols on the atmospheric thermal structure; (2) the fundamental theories of ARI reducing surface wind speed, redistributing water vapour and energy, and then modulating precipitation intensity; (3) different aerosol types (absorbing versus scattering) and aerosol concentrations causing different precipitation diurnal and weekly variations; (4) microphysical processes (cloud water competition, invigoration effect, and evaporation cooling) and observational evidence of different effects of aerosols on precipitation intensity, including enhancing, inhibiting, and transitional effects from enhancement to suppression; and (5) how meteorology, water vapor and dynamics influencing the effect of ACI and ARI on precipitation. In addition, this review lists the existing issues and future research directions for attaining a more comprehensive understanding of aerosol effects on precipitation. Overall, this review advances our understanding of aerosol effects on precipitation and could guide the improvement of weather and climate models to predict complex aerosol-precipitation interactions more accurately.

Urban and suburban decadal variations in air pollution of Beijing and its meteorological drivers.

Air pollution is a major threat to human health and ecosystems. Using 10-year (2013-2022) multi-source observations for the Beijing, China, we showed that clean-air actions have significantly reduced PM2.5, PM10, CO, NO2, and SO2 pollution, with an increase in the surface maximum daily 8-h average ozone (MDA8O3) concentrations during autumn and winter, leading to a rapid diminishment of the urban-suburban gap in air pollution. Secondary sources and vehicle emissions were enhanced in both urban and suburban areas in all seasons except summer from 2013 to 2022. By combining statistical analysis with the convergent cross-mapping model, the varying relationships between air pollution and meteorological conditions in the urban and suburban areas were delineated. The results suggested that boundary layer height and relative humidity exerted strong and stable influences on all air pollutants, except for MDA8O3, whose key meteorological driver was temperature. This study showed that increasing O3 trends in autumn and winter and aggravated O3 formation in summer in urban areas in Beijing became non-negligible from 2013 to 2022, despite the declining levels of air pollutants. Meteorological observations suggested that weather patterns in Beijing, characterized by higher temperatures, sunshine hours, and boundary layer height and lower relative humidity, have become more favorable for O3 formation in autumn and winter. Future mitigation efforts should focus on reducing VOC and NOx emissions to avoid further deterioration of O3 pollution under the frequent adverse meteorological conditions predicted under the background of global warming.

Increased aerosol scattering contributes to the recent monsoon rainfall decrease over the Gangetic Plain.

The climate effects of atmospheric aerosols remain highly uncertain. Part of the uncertainty arises from the fact that scattering and absorbing aerosols have distinct or even opposite effects. Thus their relative fraction is critical in determining the overall aerosol climate effect. This study combines observations and global model simulations to demonstrate that changes in the fraction of scattering and absorbing aerosols play an important role in driving the monsoon precipitation decrease over northern India since the 1980s, especially over the Gangetic Basin. Increased aerosol scattering, or decreased aerosol absorption, manifested as a significant increase of aerosol single scattering albedo (SSA), causes strong cooling in the upper atmosphere. This suppresses vertical convection and thus reduces precipitation. Further analysis of the Couple Model Intercomparison Project Phase 6 multi-model-mean historical simulation shows that failing to capture the SSA increase over northern India is likely an important cause of the simulated precipitation trend bias in this area.

Metal-free auxiliary pyrophosphate detection based on near-infrared carbon dots.

The excessive use of pyrophosphate (PPi) anions as additives poses a serious threat to human health and the environment. Considering the current status of PPi probes, the development of metal-free auxiliary PPi probes has important applications. In this study, a novel near-infrared nitrogen and sulfur co-doped carbon dots (N,S-CDs) were prepared. The average particle size of N,S-CDs was 2.25 ± 0.32 nm with average height was 3.05 nm. The probe N,S-CDs showed a special response to PPi, and a good linear relationship was obtained with PPi concentrations ranging from 0 to 1 µM, with the limit of detection being 0.22 nM. Tap water and milk were used for practical inspection, and ideal experimental results were acquired. In addition, the probe N,S-CDs also showed good results in biological systems, such as cell and zebrafish experiments.

Characterization of global fire activity and its spatiotemporal patterns for different land cover types from 2001 to 2020.

Fire is a widespread phenomenon that plays an important role in Earth's ecosystems. This study investigated the global spatiotemporal patterns of burned areas, daytime and nighttime fire counts, and fire radiative power (FRP) from 2001 to 2020. The month with the largest burned area, daytime fire count, and FRP presented a bimodal distribution worldwide, with dual peaks in early spring (April) and summer (July and August), while the month with the largest nighttime fire count and FRP showed a unimodal distribution, with a peak in July. Although the burned area showed decline at the global scale, a significant increase occurred in temperate and boreal forest regions, where nighttime fire occurrence and intensity have consistently increased in recent years. The relationships among burned area, fire count, and FRP were further quantified in 12 typical fire-prone regions. The burned area and fire count exhibited a humped relationship with FRP in most tropical regions, whereas the burned area and fire count constantly increased when the FRP was below approximately 220 MW in temperate and boreal forest regions. Meanwhile, the burned area and FRP generally increased with the fire count in most fire-prone regions, indicating an increased risk of more intense and larger fires as the fire count increased. The spatiotemporal dynamics of burned areas for different land cover types were also explored in this study. The results suggest that the burned areas in forest, grassland, and cropland showed dual peaks in April and from July to September while the burned areas in shrubland, bareland, and wetlands usually peaked in July or August. Significant increases in forest burned area were observed in temperate and boreal forest regions, especially in the western U.S. and Siberia, whereas significant increases in cropland burned area were found in India and northeastern China.

Multi-scene visual hydrazine hydrate detection based on a dibenzothiazole derivative.

Hydrazine (N2H4) produced by industries is distributed into different environments, and seriously threatens ecology and human security. Hence, it is important to develop probes that detect N2H4 in various environments. In this study, a novel N2H4 fluorescent probe was prepared based on a dibenzothiazole derivative (DBTD). The obtained DBTD probe demonstrated a strong ratio of colorimetric detection of N2H4, a rapid response, and good selectivity and sensitivity (detection limit 0.438 µM). Based on its good performance, the DBTD probe was successfully applied for the determination of trace N2H4 in water, cells, and zebrafish. In addition, the results of the fluorescence colocalization experiment demonstrated the lysosomal-targetable ability of DBTD.

Long-Term Variations in Global Solar Radiation and Its Interaction with Atmospheric Substances at Qomolangma.

An empirical model to estimate global solar radiation was developed at Qomolangma Station using observed solar radiation and meteorological parameters. The predicted hourly global solar radiation agrees well with observations at the ground in 2008-2011. This model was used to calculate global solar radiation at the ground and its loss in the atmosphere due to absorbing and scattering substances in 2007-2020. A sensitivity analysis shows that the responses of global solar radiation to changes in water vapor and scattering factors (expressed as water-vapor pressure and the attenuation factor, AF, respectively) are nonlinear, and global solar radiation is more sensitive to changes in scattering than to changes in absorption. Further applying this empirical model, the albedos at the top of the atmosphere (TOA) and the surface in 2007-2020 were computed and are in line with satellite-based retrievals. During 2007-2020, the mean estimated annual global solar radiation increased by 0.22% per year, which was associated with a decrease in AF of 1.46% and an increase in water-vapor pressure of 0.37% per year. The annual mean air temperature increased by about 0.16 °C over the 14 years. Annual mean losses of solar radiation caused by absorbing and scattering substances and total loss were 2.55, 0.64, and 3.19 MJ m-2, respectively. The annual average absorbing loss was much larger than the scattering loss; their contributions to the total loss were 77.23% and 22.77%, indicating that absorbing substances play significant roles. The annual absorbing loss increased by 0.42% per year, and scattering and total losses decreased by 2.00% and 0.14% per year, respectively. The estimated and satellite-derived annual albedos increased at the TOA and decreased at the surface. This study shows that solar radiation and its interactions with atmospheric absorbing and scattering substances have played key but different roles in regional climate and climate change at the three poles.

Magnetic solid-phase extraction of pyrethroid and neonicotinoid insecticides separately in environmental water samples based on alkaline or acidic group-functionalized mesoporous silica.

Pyrethroids and neonicotinoids are widely used insecticides. However, their residues have unfavorable effects on ecological systems. Magnetic solid phase extraction is a reliable pretreatment method for a better detection of insecticides at low concentrations. In this work, amino- and carboxyl-functionalized magnetic KIT-6 were designed according to the electron-accepting groups of pyrethroid molecules and electron-donating groups with neonicotinoid structures. The characterization of these two materials was conducted using transmission electron microscopy, scanning electron microscopy, nitrogen adsorption-desorption analysis, etc. The aminated composite was applied to the magnetic solid phase extraction of pyrethroid insecticides while the carboxylic one was applied to neonicotinoids, and the adsorption effects were comprehensively compared for the first time. The material dosage, solution pH, and some factors that influenced the recovery were studied and optimized. The adsorption processes of the materials were all spontaneous and exothermic. They also fitted well with pseudo-second order kinetics and Langmuir adsorption isotherms. Both physical (pore function) and chemical (electrostatic interaction) adsorption mechanisms were present in the process. The two nanocomposites were then successfully used to enrich the two kinds of insecticides in environmental water samples. The proposed method has great application potential for insecticide monitoring in complex environmental samples.

Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol-cloud interaction.

SignificanceAerosol-cloud interaction affects the cooling of Earth's climate, mostly by activation of aerosols as cloud condensation nuclei that can increase the amount of sunlight reflected back to space. But the controlling physical processes remain uncertain in current climate models. We present a lidar-based technique as a unique remote-sensing tool without thermodynamic assumptions for simultaneously profiling diurnal aerosol and water cloud properties with high resolution. Direct lateral observations of cloud properties show that the vertical structure of low-level water clouds can be far from being perfectly adiabatic. Furthermore, our analysis reveals that, instead of an increase of liquid water path (LWP) as proposed by most general circulation models, elevated aerosol loading can cause a net decrease in LWP.

Machine learning-based estimation of ground-level NO2 concentrations over China.

Most current scientific research on NO2 remote sensing focuses on tropospheric NO2 column concentrations rather than ground-level NO2 concentrations; however, ground-level NO2 concentrations are more related to anthropogenic emissions and human health. This study proposes a machine learning estimation method for retrieving the ground-level NO2 concentrations throughout China based on the tropospheric NO2 column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020. This method adopts the XGBoost machine learning model characterized by a strong fitting ability and complex model structure, which can explain the complex nonlinear and high-order relationships between ground-measured NO2 and its influencing factors. The R2 values between the retrievals and the validation and test datasets are 0.67 and 0.73, respectively, which suggests that the proposed method can reliably retrieve the ground-level NO2 concentrations across China. The distribution characteristics, seasonal variations and interannual differences in ground-level NO2 concentrations are further analyzed based on the retrieval results, demonstrating that the ground-level NO2 concentrations exhibit significant geographical and seasonal variations, with high concentrations in winter and low concentrations in summer, and the highly polluted regions are concentrated mainly in Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), Cheng-Yu District (CY) and other urban agglomerations. Finally, the interannual variation in the ground-level NO2 concentrations indicates that pollution decreased continuously from 2018 to 2020.

Health risk and disease burden attributable to long-term global fine-mode particles.

Particulate matter 2.5 (PM2.5) pollution has long been a global environmental problem and still poses a great threat to public health. This study investigates global spatiotemporal variations in PM2.5 using the newly developed satellite-derived PM2.5 dataset from 1998 to 2018. An integrated exposure-response (IER) model was employed to examine the characteristics of PM2.5-related deaths caused by chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), lung cancer (LC), and stroke in adults (age≥25), as well as lower respiratory infection (LRI) in children (age≤5). The results showed that high annual PM2.5 concentrations were observed mainly in East Asia and South Asia. Over the 19-year period, PM2.5 concentrations constantly decreased in developed regions, but increased in most developing regions. Approximately 84% of the population lived in regions where PM2.5 concentrations exceeded 10 µg/m3. Meanwhile, the vast majority of the population (>60%) in East and South Asia was consistently exposed to PM2.5 levels above 35 µg/m3. PM2.5 exposure was linked to 3.38 (95% UI: 3.05-3.70) million premature deaths globally in 2000, a number that increased to 4.11 (95% UI: 3.55-4.69) million in 2018. Premature deaths related to PM2.5 accounted for 6.54%-7.79% of the total cause of deaths worldwide, with a peak in 2011. Furthermore, developing regions contributed to the majority (85.95%-95.06%) of PM2.5-related deaths worldwide, and the three highest-ranking regions were East Asia, South Asia, and Southeast Asia. Globally, IHD and stroke were the two main contributors to total PM2.5-related deaths, followed by COPD, LC, and LRI.

The Role of Primary Emission and Transboundary Transport in the Air Quality Changes During and After the COVID-19 Lockdown in China.

In late January 2020, China's rapid and strict control measures to curb the COVID-19 spread led to a sharp halt in socio-economic activity and a significant reduction in emissions. Using the ground-based observational data, the authors synergistically quantify the nation-wide variations of major air pollutant as well as meteorology during and after the lockdown. Their concentrations (except O3) exhibited significant reduction during February and March 2020, by more than 24% during the lockdown compared with the earlier time period and by more than 17% compared with that in the same period in 2019. In contrast, ozone increased rapidly by about 60% across the country during the lockdown. Abnormal increases in carbon monoxide and particulate matter concentrations in southwest China are attributed to the severe wildfires in Southeast Asia. The concentration of air pollutants bounced back rapidly after the full-scale reopen in March 2020, indicating the decisive role of emissions in the pollution formation.

A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2.5.

Being able to monitor PM2.5 across a range of scales is incredibly important for our ability to understand and counteract air pollution. Remote monitoring PM2.5 using satellite-based data would be incredibly advantageous to this effort, but current machine learning methods lack necessary interpretability and predictive accuracy. This study details the development of a new Spatial-Temporal Interpretable Deep Learning Model (SIDLM) to improve the interpretability and predictive accuracy of satellite-based PM2.5 measurements. In contrast to traditional deep learning models, the SIDLM is both "wide" and "deep." We comprehensively evaluated the proposed model in China using different input data (top-of-atmosphere (TOA) measurements-based and aerosol optical depth (AOD)-based, with or without meteorological data) and different spatial resolutions (10 km, 3 km, and 250 m). TOA-based SIDLM PM2.5 achieved the best predictive accuracy in China, with root-mean-square errors (RMSE) of 15.30 and 15.96 µg/m3, and R2 values of 0.70 and 0.66 for PM2.5 predictions at 10 km and 3 km spatial resolutions, respectively. Additionally, we tested the SIDLM in PM2.5 retrievals at a 250 m spatial resolution over Beijing, China (RMSE = 16.01 µg/m3, R2 = 0.62). Furthermore, SIDLM demonstrated higher accuracy than five machine learning inversion methods, and also outperformed them regarding feature extraction and the interpretability of its inversion results. In particular, modeling results indicated the strong influence of the Tongzhou district on the principle PM2.5 in the Beijing urban area. SIDLM-extracted temporal characteristics revealed that summer months (June-August) might have contributed less to PM2.5 concentrations, indicating the limited accumulation of PM2.5 in these months. Our study shows that SIDLM could become an important tool for other earth observation data in deep learning-based predictions and spatiotemporal analysis.

Understanding global changes in fine-mode aerosols during 2008-2017 using statistical methods and deep learning approach.

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.

Atmospheric inverse estimates of CO emissions from Zhengzhou, China.

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.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism.

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.

Retrieving the microphysical properties of opaque liquid water clouds from CALIOP measurements.

Cloud droplet effective radius (CER) and number concentration (CDNC) are two critical microphysical properties of liquid water clouds, which play essential roles in the Earth's radiative energy balance and atmospheric hydrological cycle. Even though many satellite remote sensing techniques have been developed to obtain these two properties, the observations are often limited to the daytime. In this study, a method to estimate CER and CDNC of liquid water clouds over global ocean area during both daytime and nighttime from CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) measurements is presented. The size sensitivity of the dual-wavelength (532 nm & 1064 nm) layer-integrated attenuated backscattering signals from CALIOP is checked and information content for liquid water cloud CER retrieval is found. Taking use of the artificial neural network (ANN) technique, the CER and then the CDNC are estimated from CALIOP by combining the polarization ratio and the dual wavelength attenuated backscattering signals. The comparisons with CER and CDNC estimated from MODIS (Moderate Resolution Imaging Spectroradiometer) during daytime demonstrate the feasibility of this new method. Both the daytime and nighttime CER and CDNC derived from CALIOP observations are presented in this paper and the day-night variation of liquid water cloud is discussed which would provide useful day-night variation of liquid water cloud properties.

Increased Arctic cloud longwave emissivity associated with pollution from mid-latitudes.

There is consensus among climate models that Arctic climate is particularly sensitive to anthropogenic greenhouse gases and that, over the next century, Arctic surface temperatures are projected to rise at a rate about twice the global mean. The response of Arctic surface temperatures to greenhouse gas thermal emission is modified by Northern Hemisphere synoptic meteorology and local radiative processes. Aerosols may play a contributing factor through changes to cloud radiative properties. Here we evaluate a previously suggested contribution of anthropogenic aerosols to cloud emission and surface temperatures in the Arctic. Using four years of ground-based aerosol and radiation measurements obtained near Barrow, Alaska, we show that, where thin water clouds and pollution are coincident, there is an increase in cloud longwave emissivity resulting from elevated haze levels. This results in an estimated surface warming under cloudy skies of between 3.3 and 5.2 W m(-2) or 1 and 1.6 degrees C. Arctic climate is closely tied to cloud longwave emission, but feedback mechanisms in the system are complex and the actual climate response to the described sensitivity remains to be evaluated.