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Accurate prediction of fine particulate matter (PM2.5) concentration is crucial for improving environmental conditions and effectively controlling air pollution. However, some existing studies could ignore the nonlinearity and spatial correlation of time series data observed from stations, and it is difficult to avoid the redundancy between features during feature selection. To further improve the accuracy, this study proposes a hybrid model based on empirical mode decomposition (EMD), minimal-redundancy-maximal-relevance (mRMR), and geographically weighted neural network (GWNN) for hourly PM2.5 concentration prediction, named EMD-mRMR-GWNN. Firstly, the original PM2.5 concentration sequence with distinct nonlinearity and non-stationarity is decomposed into multiple intrinsic mode functions (IMFs) and a residual component using EMD. IMFs are further classified and reconstructed into high-frequency and low-frequency components using the one-sample t-test. Secondly, the optimal feature subset is selected from high-frequency and low-frequency components with mRMR for the prediction model, thus holding the correlation between features and the target variable and reducing the redundancy among features. Thirdly, the residual component is predicted with the simple moving average (SMA) due to its strong trend and autocorrelation, and GWNN is used to predict the high-frequency and low-frequency components. The final prediction of the PM2.5 concentration value is calculated by an artificial neural network (ANN) composed of the predictive values of each component. PM2.5 concentration prediction experiments in three representational cities, such as Beijing, Wuhan, and Kunming were carried out. The proposed model achieved high accuracy with a coefficient of determination greater than 0.92 in forecasting PM2.5 concentration for the next 1 h. We compared this model with four baseline models in forecasting PM2.5 concentration for the next few hours and found it performed the best in PM2.5 concentration prediction. The experimental results indicated the proposed model can improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Surface air temperature (Ta), as an important climate variable, has been used in a wide range of fields such as ecology, hydrology, climatology, epidemiology, and environmental science. However, ground measurements are limited by poor spatial representation and inconsistency, while reanalysis and meteorological forcing datasets suffer from coarse spatial resolution and inaccuracy. Previous studies using satellite data have mainly estimated Ta under clear-sky conditions, or with limited temporal and spatial coverage. In this study, an all-sky daily mean Ta product at 1 km spatial resolution over mainland China for 2003-2019 has been generated mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. Three Ta estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The validation results showed that R2 and root mean square error (RMSE) values of the three models ranged from 0.984 to 0.986 and 1.342 K to 1.440 K, respectively. We examined the spatiotemporal patterns and land cover type dependences of model accuracy. The relative contributions of different features to models were also quantitatively analysed. Finally, values of our Ta product in 2010 were validated and compared with the China Land Data Assimilation System (CLDAS) dataset at 0.0625° spatial resolution, China Meteorological Forcing Data (CMFD) dataset at 0.1° spatial resolution and GLDAS dataset at 0.25° spatial resolution. The R2 and RMSE values of our product were 0.992 and 1.010 K, respectively, indicating this high-resolution satellite product has significantly higher accuracy. In summary, the all-sky daily mean Ta dataset developed in this study has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in the studies of climate change and hydrological cycle. This dataset is freely available at http://doi.org/10.5281/zenodo.4399453 (Chen et al., 2021b) and the University of Maryland (http://glass.umd.edu/Ta%5FChina/) currently. A sub-dataset that covers Beijing generated from this dataset is publicly available at http://doi.org/10.5281/zenodo.4405123 (Chen et al., 2021a). [ABSTRACT FROM AUTHOR]

Beijing-Tianjin-Hebei metropolitan area is a significant carbon emission center. The region's early achievement of peak carbon targets is critical to the nation's achievement of peak carbon targets. In this paper, it is proposed to use different orders of grey models to classify into three scenarios. Based on three scenarios, the grey multivariate convolutional model with new information priority accumulation is adopted to predict carbon emissions in the Beijing-Tianjin-Hebei region and select the scenario suitable for local development. The results show that: (1) The Beijing region has already achieved peak carbon, the Tianjin region may not reach its peak carbon target by 2030, and the Hebei region is expected to reach its peak carbon target by 2030. (2) The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. The low-speed growth carbon emission scenario is more in line with the future development of Tianjin city. The low-rate carbon reduction scenario is more in line with the synergistic governance of pollution reduction and carbon reduction in Hebei Province. (3) Beijing's population policy in the most recent years has been conducive to improving the local environment. Tianjin's medium-term population policy is more in line with the local area. Hebei's medium-term industrial structure reform is favorable to local development. • Grey correlation analysis is used to analyze the factors affected carbon emissions in a region. • The factors with high correlation are predicted by using grey prediction models of different orders. • The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. [ABSTRACT FROM AUTHOR]

Atmospheric nanoparticle growth is a critical bridge between aerosol to clouds and climate. However, current mechanisms for nanoparticle growth are not yet fully understood due to unknown size-resolved chemical composition during the growth process. Here, we present comprehensive field measurements of the size-resolved chemical composition of the atmospheric nanoparticle growth in the size range from 10 to 100 nm using a custom-made Nanoparticle Chemical Ionization Mass Spectrometer (NPCIMS). The typical chemical compositions of nanoparticles are quantified in both positive and negative ion modes during growth processes after sensitivity calibration. The quantification results for chemical compositions show that CHO organics account for ∼80% of the newly formed particle in the growth process with diameters from 10 to 25 nm. However, the nitrate component is rarely observed during the nanoparticle growth under size-resolved mode (10–25 nm), indicating the little contribution of the nitrate component to growth at diameters below 25 nm. Different from the contribution of aminium salts to nanoparticle growth according to the previous urban observations in Mexico, the newly formed particle growth in urban Beijing is dominated by ammonium, sulfate, and organic components in the size-resolved modes (10–25 nm) in springtime. [Display omitted] • The newly formed particle growth in urban Beijing is dominated by ammonium, sulfate, and organic components in springtime. • The contribution of nitrate, protonated methylammonium and protonated C 2 amines to nanoparticles growth is little. • CHO organics account up to ～80% in the growth of the newly formed organic particle with diameters from 10 to 25 nm. [ABSTRACT FROM AUTHOR]

Purpose: The association between atmospheric particulate matter and emergency room visits for cerebrovascular disease were evaluated in Beijing. Methods: A generalized additive model was used to evaluate the associations between particulate matter and cerebrovascular disease, based on the daily data of meteorological elements, PM concentrations, and emergency room (ER) visits for cerebrovascular disease in Beijing from 2009 to 2012. Long-term trends and the effects of holidays, the day of the week, and confounding factors were controlled to determine the lag effect at 0–6 days. Single- and double-pollutant models were employed for different age and sex groups. Results: The effect of PM2.5 concentration on the number of daily ER visits for cerebrovascular disease was much stronger than that of PM10 concentration. PM2.5 and PM10 had maximum RR values of 1.096 and 1.054 at lag 6 for patients aged 61–75 years. For each inter-quartile range (IQR) increase in PM10 concentration, the maximum RR values for the total, males, females, aged 15–60 years, aged 61–75 years, and aged > 75 years were 1.024, 1.044, 1.043, 1.038, 1.054, and 1.032, respectively. For each IQR increase in PM2.5 concentration, the maximum RR values for the total, males, females, aged 15–60 years, aged 61–75 years, and aged > 75 years were 1.038, 1.064, 1.076, 1.054, 1.096, and 1.049, respectively. The RR values of the double-pollutant models were lower than those of the single-pollutant models. Conclusion: This study showed that the effects of PM pollution on cerebrovascular disease were different among different gender and age groups, and aged 61–75 years were mostly sensitive to particulate matters. The effects of PM2.5 on cerebrovascular disease were stronger than those of PM10. Our results can provide scientific evidence for the local government to take effective measures to improve air quality and the health of residents. [ABSTRACT FROM AUTHOR]

Intense and frequent new particle formation (NPF) events have been observed in polluted urban environments, yet the dominant mechanisms are still under debate. To understand the key species and governing processes of NPF in polluted urban environments, we conducted comprehensive measurements in downtown Beijing during January–March, 2018. We performed detailed analyses on sulfuric acid cluster composition and budget, as well as the chemical and physical properties of oxidized organic molecules (OOMs). Our results demonstrate that the fast clustering of sulfuric acid (H2SO4) and base molecules triggered the NPF events, and OOMs further helped grow the newly formed particles toward climate‐ and health‐relevant sizes. This synergistic role of H2SO4, base species, and OOMs in NPF is likely representative of polluted urban environments where abundant H2SO4 and base species usually co‐exist, and OOMs are with moderately low volatility when produced under high NOx concentrations. Plain Language Summary: Atmospheric new particle formation (NPF) is a dominant source of atmospheric ultrafine particles worldwide. Those particles profoundly influence climate and human health. NPF includes two consecutive processes, that is, the formation of new particles (∼2 nm in diameter) and their subsequent growth to larger sizes. Extensive studies conducted in the laboratory and in forested areas have shown that many gaseous species can participate in NPF, such as sulfuric acid, ammonia, amines, and oxidize organic molecules. However, the actual roles of these vapors may vary significantly from location to location and are largely unclear in urban environments. Here, based on measurements of sulfuric acid, sulfuric acid clusters, and oxidize organic molecules, we demonstrate that sulfuric acid and base molecules were responsible for the initial formation of new particles during a wintertime field campaign in Beijing. The majority of oxidized organic molecules had a minor contribution to the formation of new particles but were crucial for particle growth above 2–3 nm to climate‐ and health‐relevant sizes. Key Points: Process‐level understanding of new particle formation (NPF) in wintertime Beijing was obtained based on measurement state‐of‐the‐art instrumentsThe analysis of sulfuric acid cluster composition and budget showed that sulfuric acid‐base clustering initiated NPFCondensable organic vapors were characterized and demonstrated to have a crucial influence on the growth of newly formed particles [ABSTRACT FROM AUTHOR]

An optical design study of a bending‐magnet beamline, based on multi‐bend achromat storage ring lattices, at the High Energy Photon Source, to be built in Beijing, China, is described. The main purpose of the beamline design is to produce a micro‐scale beam from a bending‐magnet source with little flux loss through apertures. To maximize the flux of the focal spot, the synchrotron source will be 1:1 imaged to a virtual source by a toroidal mirror; a mirror pair will be used to collimate the virtual source into quasi‐parallel light which will be refocused by a Kirkpatrick–Baez mirror pair. In the case presented here, a beamline for tender X‐rays ranging from 2.1 keV to 7.8 keV, with a spot size of approximately 7 µm (H) × 6 µm (V) and flux up to 2 × 1012 photons s−1, can be achieved for the purpose of X‐ray absorption fine‐structure (XAFS)‐related experiments, such as scanning micro‐XAFS and full‐field nano‐XAFS. [ABSTRACT FROM AUTHOR]

Groundwater has always been a valuable resource in Beijing, facing a great decline of groundwater level during the past decades. However, few previous researches have revealed the spatial variation of groundwater level within Beijing Plain. In this study, spatiotemporal variation of groundwater level from 2001 to 2010 in Beijing Plain has been investigated. Factor analysis has been conducted to identify the primary influencing factor. Results showed that the groundwater level decreased by 8.41 m from 2001 to 2010, with a linear decreasing rate of 0.954 m per year averagely. Significant spatial variation characteristics have been detected. The north area suffered more groundwater depletion than the south part in general. The lowest groundwater level has been identified downstream Miyun Reservoir, central part of the Plain. Nevertheless, the most of the south part witnessed a slight revival between 2001 and 2010. This may be due to the differences of socioeconomic circumstances in the Plain. Three influencing factors, that is, “demand factor,” “supply factor,” and “loss factor,” have been identified in the water balance model. Eigenvalues of these factors are 3.563, 2.910, and 1.632, respectively, indicating that these factors influenced the groundwater system to various extents, with the demand factor being the primary one. [ABSTRACT FROM AUTHOR]