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Red beds belong to slippery formations, and their rapid identification is of great significance for major scientific and engineering issues, such as geological hazard risk assessment and rapid response to geological disasters. Existing research often identifies red beds from a qualitative or semiquantitative perspective, resulting in slow recognition speed and inaccurate recognition results, making it difficult to quickly handle landslide geological disasters. Combined with the correlation between red beds' geomorphic characteristics, mineral compositions, and chemical compositions, this study established a preliminary identification quantitative criterion based on the basic chemical composition combination rules (SiO2+Al2O3, Al2O3/SiO2, FeO+Fe2O3, Fe2O3/FeO, K2O+Na2O, Na2O/K2O, CaO+MgO, and MgO/CaO) in the red beds. Following this, we perform principal component analysis on the basic chemical composition combination rules mentioned above. The results indicate that simultaneously meeting the following principal component features can serve as a rapid quantitative criterion for distinguishing red beds from other rocks: F1=-3.36–23.55; F2=-23.00–3.11; F3=-10.12–4.88; F4=-2.21–4.52; F5=-0.97–7.30; and F=-0.67–1.89. By comparing the chemical composition combinations of 15 kinds of rocks collected from China in this study, it is proven that the quantitative criterion proposed in this study is effective. The study results can be used for rapid identification of red beds, achieving risk assessment and rapid response to geological disasters such as landslides.

A new long-term emission inventory called the Inversed Emission Inventory for Chinese Air Quality (CAQIEI) was developed in this study by assimilating surface observations from the China National Environmental Monitoring Centre (CNEMC) using an ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System. This inventory contains the constrained monthly emissions of NOx, SO2, CO, primary PM2.5, primary PM10, and non-methane volatile organic compounds (NMVOCs) in China from 2013 to 2020, with a horizontal resolution of 15 km × 15 km. This paper documents detailed descriptions of the assimilation system and the evaluation results for the emission inventory. The results suggest that CAQIEI can effectively reduce the biases in the a priori emission inventory, with the normalized mean biases ranging from −9.1 % to 9.5 % in the a posteriori simulation, which are significantly reduced from the biases in the a priori simulations (−45.6 % to 93.8 %). The calculated root-mean-square errors (RMSEs) (0.3 mg m−3 for CO and 9.4–21.1 µg m3 for other species, on the monthly scale) and correlation coefficients (0.76–0.94) were also improved from the a priori simulations, demonstrating good performance of the data assimilation system. Based on CAQIEI, we estimated China's total emissions (including both natural and anthropogenic emissions) of the six species in 2015 to be as follows: 25.2 Tg of NOx, 17.8 Tg of SO2, 465.4 Tg of CO, 15.0 Tg of PM2.5, 40.1 Tg of PM10, and 46.0 Tg of NMVOCs. From 2015 to 2020, the total emissions decreased by 54.1 % for SO2, 44.4 % for PM2.5, 33.6 % for PM10, 35.7 % for CO, and 15.1 % for NOx but increased by 21.0 % for NMVOCs. It is also estimated that the emission reductions were larger during 2018–2020 (from −26.6 % to −4.5 %) than during 2015–2017 (from −23.8 % to 27.6 %) for most of the species. In particular, the total Chinese NOx and NMVOC emissions were shown to increase during 2015–2017, especially over the Fenwei Plain area (FW), where the emissions of particulate matter (PM) also increased. The situation changed during 2018–2020, when the upward trends were contained and reversed to downward trends for the total emissions of both NOx and NMVOCs and the PM emissions over FW. This suggests that the emission control policies may be improved in the 2018–2020 action plan. We also compared CAQIEI with other air pollutant emission inventories in China, which verified our inversion results in terms of the total emissions of NOx, SO2, and NMVOCs and more importantly identified the potential uncertainties in current emission inventories. Firstly, CAQIEI suggested higher CO emissions in China, with CO emissions estimated by CAQIEI (426.8 Tg) being more than twice the amounts in previous inventories (120.7–237.7 Tg). Significantly higher emissions were also suggested over western and northeastern China for the other air pollutants. Secondly, CAQIEI suggested higher NMVOC emissions than previous emission inventories by about 30.4 %–81.4 % over the North China Plain (NCP) but suggested lower NMVOC emissions by about 27.6 %–0.0 % over southeastern China (SE). Thirdly, CAQIEI suggested lower emission reduction rates during 2015–2018 than previous emission inventories for most species, except for CO. In particular, China's NMVOC emissions were shown to have increased by 26.6 % from 2015 to 2018, especially over NCP (by 38.0 %), northeastern China (by 38.3 %), and central China (60.0 %). These results provide us with new insights into the complex variations in air pollutant emissions in China during two recent clean-air actions, which has the potential to improve our understanding of air pollutant emissions in China and their impacts on air quality. All of the datasets are available at https://doi.org/10.57760/sciencedb.13151 (Kong et al., 2023a).

The FecB gene is one of those responsible for regulating reproductive traits in sheep. This experiment was designed to explore the impact of SNPs (single nucleotide polymorphisms) within the FecB gene on both the reproductive and body size performance of sheep. In this experiment, two SNP sites were identified as C413217T and A431965G. Through analysis of genetic diversity and correlations, we aimed to identify combinations of genotypes and haplotypes that influence reproductive performance and body size performance. The most prominent genotypes associated with lambing performance were identified as AA and CT, with the dominant haplotype combination being AACT. For body size performance, the dominant genotypes were AA and CC, while the dominant haplotype combination was AACC. These advantageous genotypes and haplotype combinations are conducive to promoting the selection and improvement of sheep breeds, advancing the progress of sheep genetic breeding, and providing theoretical support for the breeding of higher-fertility sheep.

The unprecedented lockdown of human activities during the COVID-19 pandemic has significantly influenced social life in China. However, understanding the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air-pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15 km) resolutions. Subsequently, contributions of emission changes versus meteorological variations during the COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations in multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measure with NOx emissions decreasing substantially by ∼40 %. However, emissions of other air pollutants were found to only decrease by ∼10 % because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over the North China Plain (NCP) during the lockdown period, the emission change only accounted for 8.6 % of PM2.5 reductions and even led to substantial increases in O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over the NCP, which contributed 90 % of the PM2.5 reductions over most parts of the NCP region. Meanwhile, our results suggest that the local stagnant meteorological conditions, together with inefficient reductions of PM2.5 emissions, were the main drivers of the unexpected PM2.5 pollution in Beijing during the lockdown period. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

To achieve near-continuous vertical observations of volatile organic compounds (VOCs) in the planetary boundary layer (PBL), multi-channel whole-air sampling equipment onboard an unmanned aerial vehicle (UAV) platform was developed in this study. The equipment consists of a multi-position solenoid valve and specially designed lightweight quartz sampling canisters. The canisters have little adsorption loss of VOCs and good inter-canister reproducibility. The 7 d recovery test shows that most VOC species (97 %) had a 1-week decay within 20 %. Online instruments for measuring O3, NO2, CO, SO2, and meteorological parameters are also integrated into the UAV platform. During one take-off and landing, the UAV platform can reach 800 m above the ground within 40 min and take whole-air samples at six heights. Vertical profiles of VOCs and trace gases during the evolution of the PBL in south-western China are successfully obtained by deploying the newly developed UAV system.

Aerosol vertical stratification is important for global climate and planetary boundary layer (PBL) stability, and no single method can obtain spatiotemporally continuous vertical profiles. This paper develops an online data assimilation (DA) framework for the Eulerian atmospheric chemistry-transport model (CTM) Nested Air Quality Prediction Model System (NAQPMS) with the Parallel Data Assimilation Framework (PDAF) as the NAQPMS-PDAF for the first time. Online coupling occurs based on a memory-based way with two-level parallelization, and the arrangement of state vectors during the filter is specifically designed. Scaling tests demonstrate that the NAQPMS-PDAF can make efficient use of parallel computational resources for up to 25 000 processors with a weak scaling efficiency of up to 0.7. The 1-month long aerosol extinction coefficient profiles measured by the ground-based lidar and the concurrent hourly surface PM2.5 are solely and simultaneously assimilated to investigate the performance and application of the DA system. The hourly analysis and subsequent 1 h simulation are validated through lidar and surface PM2.5 measurements assimilated and not assimilated. The results show that lidar DA can significantly improve the underestimation of aerosol loading, especially at a height of approximately 400 m in the free-running (FR) experiment, with the mean bias (BIAS) changing from −0.20 (−0.14) km−1 to −0.02 (−0.01) km−1 and correlation coefficients increasing from 0.33 (0.28) to 0.91 (0.53) averaged over sites with measurements assimilated (not assimilated). Compared with the FR experiment, simultaneously assimilating PM2.5 and lidar can have a more consistent pattern of aerosol vertical profiles with a combination of surface PM2.5 and lidar, independent extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Lidar DA has a larger temporal impact than that in PM2.5 DA but has deficiencies in subsequent quantification on the surface PM2.5. The proposed NAQPMS-PDAF has great potential for further research on the impact of aerosol vertical distribution.

A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM2.5, PM10, SO2, NO2, CO, and O3) for the period 2013–2018 at high spatial (15 km×15 km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52–0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4–39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74–0.86 and RMSE =16.8–33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM2.5 concentrations, we show that the accuracy of the PM2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users.

Nitrate, sulfate and ammonium (NSA) are the main secondary inorganic aerosols of PM2.5 and play an important role in air pollution. In this study, a 3-year observational experiment was conducted from 1 January 2015 to 31 December 2017, in Chengdu, southwest China. NSA pollution characteristics, chemical conversion generation, emission reduction control sensitivity and pollutant regional transport characteristics were analyzed. NSA are the most important chemical compositions of particles with aerodynamic equivalent diameter ≤2.5 µm in ambient air (PM2.5), and the contribution of nitrate to the accumulation of PM2.5 concentration is greater than that of sulfate and ammonium. NSA also have obvious characteristics of annual, monthly, seasonal, diurnal and weekly variations. Through observation data and model simulation, it was also found that the existence of an aerosol aqueous environment plays an important role in the formation and existence of NSA. Sensitivity analysis between NSA found that controlling NO3- and SO42- plays an important role in reducing the contribution of NSA to PM2.5, which also implies that the current control of NOx and SO2 is important for improving air pollution. Combined with meteorological conditions and potential source contribution function (PSCF) analysis, local emissions and regional emissions of pollutants are found to have important impacts on Chengdu's atmospheric environment. This research result not only provides an assessment of the current atmospheric emission reduction effect but also provides an important reference for atmospheric pollution control.

Despite the significant progress in improving chemical transport models (CTMs), applications of these modeling endeavors are still subject to large and complex model uncertainty. The Model Inter-Comparison Study for Asia III (MICS-Asia III) has provided the opportunity to assess the capability and uncertainty of current CTMs in East Asian applications. In this study, we have evaluated the multi-model simulations of nitrogen dioxide (NO2), carbon monoxide (CO) and ammonia (NH3) over China under the framework of MICS-Asia III. A total of 13 modeling results, provided by several independent groups from different countries and regions, were used in this study. Most of these models used the same modeling domain with a horizontal resolution of 45 km and were driven by common emission inventories and meteorological inputs. New observations over the North China Plain (NCP) and Pearl River Delta (PRD) regions were also available in MICS-Asia III, allowing the model evaluations over highly industrialized regions. The evaluation results show that most models captured the monthly and spatial patterns of NO2 concentrations in the NCP region well, though NO2 levels were slightly underestimated. Relatively poor performance in NO2 simulations was found in the PRD region, with larger root-mean-square error and lower spatial correlation coefficients, which may be related to the coarse resolution or inappropriate spatial allocations of the emission inventories in the PRD region. All models significantly underpredicted CO concentrations in both the NCP and PRD regions, with annual mean concentrations that were 65.4 % and 61.4 % underestimated by the ensemble mean. Such large underestimations suggest that CO emissions might be underestimated in the current emission inventory. In contrast to the good skills for simulating the monthly variations in NO2 and CO concentrations, all models failed to reproduce the observed monthly variations in NH3 concentrations in the NCP region. Most models mismatched the observed peak in July and showed negative correlation coefficients with the observations, which may be closely related to the uncertainty in the monthly variations in NH3 emissions and the NH3 gas–aerosol partitioning. Finally, model intercomparisons have been conducted to quantify the impacts of model uncertainty on the simulations of these gases, which are shown to increase with the reactivity of species. Models contained more uncertainty in the NH3 simulations. This suggests that for some highly active and/or short-lived primary pollutants, like NH3, model uncertainty can also take a great part in the forecast uncertainty in addition to the emission uncertainty. Based on these results, some recommendations are made for future studies.

Spatiotemporal variations of ozone (O3) and nitrogen oxide (NOx) mixing ratios from 14 state-of-the-art chemical transport models (CTMs) are intercompared and evaluated with O3 observations in East Asia, within the framework of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III). This study was designed to evaluate the capabilities and uncertainties of current CTMs simulations for Asia and to provide multi-model estimates of pollutant distributions. These models were run by 14 independent groups working in China, Japan, South Korea, the United States and other countries/regions. Compared with the previous phase of MICS-Asia (MICS-Asia II), the evaluation with observations was extended from 4 months to 1 full year across China and the western Pacific Rim. In general, model performance levels for O3 varied widely by region and season. Most models captured the key patterns of monthly and diurnal variation of surface O3 and its precursors in the North China Plain and western Pacific Rim but failed to do so for the Pearl River Delta. A significant overestimation of surface O3 was evident from May to September/October and from January to May over the North China Plain, the western Pacific Rim and the Pearl River Delta. Comparisons drawn from observations show that the considerable diversity in O3 photochemical production partly contributed to this overestimation and to high levels of inter-model variability in O3 for North China. In terms of O3 soundings, the ensemble average of models reproduced the vertical structure for the western Pacific, but overestimated O3 levels to below 800 hPa in the summer. In the industrialized Pearl River Delta, the ensemble average presented an overestimation for the lower troposphere and an underestimation for the middle troposphere. The ensemble average of 13 models for O3 did not always exhibit superior performance compared with certain individual models in contrast with its superior value for Europe. This finding suggests that the spread of ensemble-model values does not represent all of the uncertainties of O3 or that most MICS-Asia III models missed key processes. This study improved the performance of modeling O3 in March at Japanese sites compared with MICS-Asia II. However, it overpredicted surface O3 concentrations for western Japan in July, which was not found by MICS-Asia II. Major challenges still remain with regard to identifying the sources of bias in surface O3 over East Asia in CTMs.

The majority of individuals with Strongyloides stercoralis (S. stercoralis) colitis are clinically asymptomatic. Symptomatic individuals may complain of nonspecific gastrointestinal symptoms, such as abdominal pain, intermittent or persistent diarrhea, nausea, or loss of appetite. In addition, skin manifestations such as recurrent urticaria can occur. If infection is not diagnosed and left untreated, it can lead to chronic inflammation of the gastrointestinal tract. It is well documented that chronic colitis such as inflammatory bowel disease can predispose individuals to colorectal cancer. Additionally, there is evidence of chronic parasitic infections inducing the development of cancers in other organs within the gastrointestinal tract. In this case vignette, we describe a case of chronic Strongyloides stercoralis infection in a Peruvian woman presenting with colorectal cancer.

A historic haze event, characterized by lengthy, large-scale and severe pollution, occurred in the Yangtze River Delta (YRD) of China from 1 to 10 December 2013. This haze event significantly influenced air quality throughout the region, especially in urban areas. Aerosol physical, chemical and optical properties were measured in Shanghai. Sometimes the 1 h average particle concentration (e.g., PM2.5) exceeded 600 µg m−3. Inorganic water-soluble ions in particles, trace gases and aerosol optical coefficients had a similar tendency to increase evidently from clear to hazy episodes. A combination of various factors contributed to the formation and evolution of the haze event, among which meteorological conditions, local anthropogenic emissions and pollutants are the major factors. High pressure system, calm surface wind and subsidence airflow were responsible for the decrease of planetary boundary layer (PBL) and the accumulation of pollutants. Atmospheric visibility correlated strongly with relative humidity (RH), particle number in size of 600–1400 nm other than particulate water-soluble species and particle mass (PM2.5). The particle hygroscopicity plays an important role in atmospheric visibility reduction. The results are somewhat helpful to forecast and eliminate regional atmospheric pollution in China.

Flash floods pose significant hazards in urbanised zones and have important implications financially and for humans alike in both the present and future due to the likelihood that global climate change will exacerbate their consequences. It is thus of crucial importance to improve the models of these phenomena especially when they occur in heterogeneous and karst basins where they are difficult to describe physically. Toward this goal, this paper applies a recent methodology (Knowledge eXtraction (KnoX) methodology) dedicated to extracting knowledge from a neural network model to better determine the contributions and time responses of several well-identified geographic zones of an aquifer. To assess the interest of this methodology, a case study was conducted in southern France: the Lez hydrosystem whose river crosses the conurbation of Montpellier (400 000 inhabitants). Rainfall contributions and time transfers were estimated and analysed in four geologically delimited zones to estimate the sensitivity of flash floods to water coming from the surface or karst. The Causse de Viols-le-Fort is shown to be the main contributor to flash floods and the delay between surface and underground flooding is estimated to be 3 h. This study will thus help operational flood warning services to better characterise critical rainfall and develop measurements to design efficient flood forecasting models. This generic method can be applied to any basin with sufficient rainfall–run-off measurements.

Flash floods forecasting in the Mediterranean area is a major economic and societal issue. Specifically, considering karst basins, heterogeneous structure and nonlinear behaviour make the flash flood forecasting very difficult. In this context, this work proposes a methodology to estimate the contribution from karst and non-karst components using toolbox including neural networks and various hydrological methods. The chosen case study is the flash flooding of the Lez river, known for his complex behaviour and huge stakes, at the gauge station of Lavallette, upstream of Montpellier (400 000 inhabitants). After application of the proposed methodology, discharge at the station of Lavallette is spited between hydrographs of karst flood and surface runoff, for the two events of 2014. Generalizing the method to future events will allow designing forecasting models specifically for karst and surface flood increasing by this way the reliability of the forecasts.

Flash floods are often responsible for many deaths and involve many material damages. Regarding Mediterranean karst aquifers, the complexity of connections, between surface and groundwater, as well as weather non-stationarity patterns, increase difficulties in understanding the basins behaviour and thus warning and protecting people. Furthermore, given the recent changes in land use and extreme rainfall events, knowledge of the past floods is no longer sufficient to manage flood risks. Therefore the worst realistic flood that could occur should be considered. Physical and processes-based hydrological models are considered among the best ways to forecast floods under diverse conditions. However, they rarely match with the stakeholders' needs. In fact, the forecasting services, the municipalities, and the civil security have difficulties in running and interpreting data-consuming models in real-time, above all if data are uncertain or non-existent. To face these social and technical difficulties and help stakeholders, this study develops two operational tools derived from these models. These tools aim at planning real-time decisions given little, changing, and uncertain information available, which are: (i) a hydrological graphical tool (abacus) to estimate flood peak discharge from the karst past state and the forecasted but uncertain intense rainfall; (ii) a GIS-based method (MARE) to estimate the potential flooded pathways and areas, accounting for runoff and karst contributions and considering land use changes. Then, outputs of these tools are confronted to past and recent floods and municipalities observations, and the impacts of uncertainties and changes on planning decisions are discussed. The use of these tools on the recent 2014 events demonstrated their reliability and interest for stakeholders. This study was realized on French Mediterranean basins, in close collaboration with the Flood Forecasting Services (SPC Med-Ouest, SCHAPI, municipalities).

Measurements of cloud condensation nuclei (CCN), condensation nuclei (CN) and aerosol chemical composition were performed simultaneously at an urban site in Shanghai from 6 to 9 November 2010. The variations of CCN number concentration (NCCN) and aerosol activity (activated aerosol fraction, NCCN/NCN) were examined during a fog–haze co-occurring event. Anthropogenic pollutants emitted from vehicles and unfavorable meteorological conditions such as low planetary boundary layer (PBL) height exerted a great influence on PM2.5 and black carbon (BC) loadings. NCCN at 0.2% supersaturation (SS) mostly fell in the range of 994 to 6268 cm−3, and the corresponding NCCN/NCN varied between 0.09 and 0.57. NCCN and NCCN/NCN usually were usually higher in the hazy case due to increased aerosol concentration in the accumulation mode (100–500 nm), and lower in the foggy–hazy and clear cases. The BC mass concentration posed a strong positive effect on NCCN in the foggy–hazy and hazy cases, whereas it poorly correlated with NCCN in the clear case. NCCN/NCN was weakly related with BC in both foggy–hazy and hazy cases. By using a simplified particle hygroscopicity (κ), the calculated critical dry size (CDS) of activated aerosol did not exceed 130 nm at 0.2% SS in spite of diverse aerosol chemical compositions. The predicted NCCN at 0.2% SS was very successful compared with the observed NCCN in clear case (R2=0.96) and foggy–hazy/hazy cases (R2=0.91). In addition, their corresponding ratios of predicted to observed NCCNwere on average 0.95 and 0.92, respectively. More organic matter is possibly responsible for this closure difference between foggy–hazy/hazy and clear cases. These results reveal that the particulate pollutant burden exerts a significant impact on NCCN, especially NCCN/NCN promotes effectively during the polluted periods.

New particle formation (NPF) events and their impacts on cloud condensation nuclei (CCN) were investigated using continuous measurements collected in urban Shanghai from 1 to 30 April 2012. During the campaign, NPF occurred in 8 out of the 30 days and enhanced CCN number concentration (NCCN) by a factor of 1.2–1.8, depending on supersaturation (SS). The NPF event on 3 April 2012 was chosen as an example to investigate the NPF influence on CCN activity. In this NPF event, secondary aerosols were produced continuously and increased PM2.5 mass concentration at a rate of 4.33 μg cm−3 h−1, and the growth rate (GR) and formation rate (FR) were on average 5 nm h−1 and 0.36 cm−3 s−1, respectively. The newly formed particles grew quickly from nucleation mode (10–20 nm) into CCN size range. NCCN increased rapidly at SS of 0.4–1.0% but weakly at SS of 0.2%. Correspondingly, aerosol CCN activities (fractions of activated aerosol particles in total aerosols, NCCN/NCN) were significantly enhanced from 0.24–0.60 to 0.30–0.91 at SS of 0.2–1.0% due to the NPF. On the basis of the κ-Köhler theory, aerosol size distributions and chemical composition measured simultaneously were used to predict NCCN. There was a good agreement between the predicted and measured NCCN (R2=0.96, Npredicted/Nmeasured=1.04). This study reveals that NPF exerts large impacts on aerosol particle abundance and size spectra; thus, it significantly promotes NCCN and aerosol CCN activity in this urban environment. The GR of NPF is the key factor controlling the newly formed particles to become CCN at all SS levels, whereas the FR is an effective factor only under high SS (e.g., 1.0%) conditions.

Controlled bench scale pulverized coal combustion studies were performed, demonstrating that inorganic particles play a critical role as carriers of organic species. Two commonly-used aerosol mass spectrometry techniques were applied to characterize fine particle formation during coal combustion. It was found that the organic species in coal combustion aerosols have mass spectra similar to those generated by biomass combustion. Ambient measurements in Shanghai, China confirm the presence of these species in approximately 29–38% of the sampled particles. With the absence of major biomass sources in the Shanghai area, it is suggested that coal combustion may be the main source of these particles. This work indicates there is a significant potential for incorrect apportionment of coal combustion particles to biomass burning sources using widely adopted mass spectrometry techniques.

Recent studies have demonstrated atmospheric deposition as an important source of bioreactive compounds to the ocean. The South China Sea (SCS), where aerosol loading is among the highest in the world, however, is poorly studied, particularly on the in situ response of phytoplankton community structures to atmospheric deposition. By conducting a series of microcosm bioassays at different hydrographical locations and simulating different aerosol event scales, we observed both positive and negative responses to the input of East Asian (EA) aerosol with high nitrogen (N) and trace metal contents, in terms of biomass, composition and physiological characteristics of phytoplankton communities. High levels of aerosol loading relieved phytoplankton nitrogen and trace metal limitations in SCS, and thus increased total phytoplankton biomass, enhanced their physiological indicators (e.g. photosynthetic efficiency) and shifted phytoplankton assemblages from being dominated by picoplankton to microphytoplanton, especially diatoms. However, under low levels of aerosol loading, the composition shift and biomass accumulation were not apparent, suggesting that the stimulation effects might be counterbalanced by enhanced grazing mortality indicated by increased abundance of protist grazers. Trace metal toxicity of the aerosols might also be the reason for the reduction of picocyanobacteria when amended with high EA aerosols. The magnitude and duration of the deposition event, as well as the hydrographical and trophic conditions of receiving waters are also important factors when predicting the influence of an aerosol deposition event. Our results demonstrated different responses of phytoplankton and microbial food web dynamics to different scales of atmospheric input events in SCS and highlighted the need for achieving an accurate comprehension of atmospheric nutrient on the biogeochemical cycles of the oceans.