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The mechanism of aerosol pollution transport remains highly elusive owing to the myriad of influential factors. In this study, ground station data, satellite data, ground-based LiDAR remote sensing data, sounding data, ERA5 reanalysis and a backward trajectory model were combined to investigate the formation process and optical properties of winter aerosol pollution in Beijing and surrounding areas. The analysis of ground station data shows that compared to 2019 and 2021, the pandemic lockdown policy resulted in a decrease in the total number of pollution days and a decrease in the average concentration of particulate matter in the Beijing area in 2020. The terrain characteristics of the Beijing-Tianjin-Hebei (BTH) made it prone to northeast and southwest winds. The highest incidence of aerosol pollution in Beijing occurs in February and March during the spring and winter seasons. Analysis of a typical heavy aerosol pollution process in the Beijing area from 28 February to 5 March 2019 shows that dust and fine particulate matter contributed to the primary pollution; surface air temperature inversion and an average wind speed of less than 3 m/s were conducive to the continuous accumulation of pollutants, which was accompanied by the oxidation reaction of NO2 and O3, forming photochemical pollution. The heavy aerosol pollution was transmitted and diffused towards the southeast, gradually eliminating the pollution. Our results provide relevant research support for the prevention and control of aerosol pollution. [ABSTRACT FROM AUTHOR]

This paper investigated the dynamic relationship between ife expectancy (LE) and its inflencing factors including, health care, socioeconomic, and environment factors in Beijing City of China. Time-series data from 2000 to 2018 were collected and vector autoregression (VAR) modeling was performed. This study quantified the lagged effect of air pollution and socioeconomic factors on increased LE over the 19 years of the study period in Beijing. The results showed that a VAR model with optimum lag 3 was constructed between LE and three explanatory variables including per capita area of green land (AGL), the average wage of employed staff and workers (WAGE), and PM2.5. In addition, LE showed a decrease from its present value once the impulse of AGL and WAGE are given. In contrast, LE increase from its present value after the impulse of PM2.5 given in the ten-year period analysis. In sum, environment factors such as air pollution and area of green land are considered to be highly effective in influencing LE than socioeconomic factors in Beijing City. [ABSTRACT FROM AUTHOR]

Accurately forecasting wind speed on a short-term scale has become essential in the field of wind power energy. In this paper, a multi-variable long short-term memory network model (MV-LSTM) based on Pearson correlation coefficient feature selection is proposed to predict the short-term wind speed. The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, and air pressure, to predict the wind speed in the next hour. Hourly data collected from two ground observation stations in Yanqing and Zhaitang in Beijing were divided into training and test sets. The training sets were used to train the model, and the test sets were used to evaluate the model with the root-mean-square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE) metrics. The proposed method is compared with two other forecasting methods (the autoregressive moving average model (ARMA) method and the single-variable long short-term memory network (LSTM) method, which inputs only historical wind speed data) based on the same dataset. The experimental results prove the feasibility of the MV-LSTM method for short-term wind speed forecasting and its superiority to the ARMA method and the single-variable LSTM method. [ABSTRACT FROM AUTHOR]

Based on typical water-rich sandy gravel strata in Beijing, in order to explore the application of the artificial ground freezing method (AGF) in urban large-scale underground engineering, the formation and development of freezing body were analyzed when multirow freezing pipes were working together, and the group effect exhibited during the freezing process was also revealed in this paper. On this basis, the basin-shaped freezing method (BFM) is put forward as an application of AGF used in underground engineering. BFM structure consists of two parts: the frozen curtain (basin wall) around the excavation scope and the horizontal frozen body (basin bottom) at the bottom of the station. Physical model test and numerical simulation were conducted to study temperature field expansion of BFM under two different conditions. The results show the following: (1) The group effect refers to the cooling effect of different freezing pipes influencing each other during freezing process. Under the condition of still water, the group effect expands the freezing area, and it shows the gradual development of freezing from back water surface to front water surface under seepage condition. (2) BFM can effectively play the role of water proofing, and although different parts of basin structure show different frozen order under different conditions, both basin wall and basin bottom can form an effective thickness during the freezing process. [ABSTRACT FROM AUTHOR]

• Determined the optimal UAV wide-band vegetation indices (VIs) for LAI and LCC retrievals. • Analyzed the sensitivity of VIs used for retrieval to influencing factors. • Constructed a hybrid inversion method of LAI and chlorophyll for individual apple tree crowns. • Quantitatively characterized the effect of canopy shading on inversion. The structural and chemical characteristics of individual apple tree crowns can indicate the nutritional and growth status of the trees, making them crucial for advancing orchard management practices. In this study, we collected multispectral imagery and ground validation data from two representative apple orchards in Beijing, China. We employed a hybrid inversion method to estimate the Leaf Area Index (LAI), Leaf Chlorophyll Content (LCC), and Canopy Chlorophyll Content (CCC) of individual apple tree crowns. Furthermore, we quantitatively evaluated the impact of canopy shading on the inversion results and mapped these traits at the scale of individual tree crowns (ITCs). To determine the optimal broad-band Vegetation Indices (VIs) for estimating LAI and LCC, we empirically analyzed 22 VIs using the PROSAIL simulation dataset. We constructed two measured datasets of canopy reflectance by masking canopy shadows, one containing shaded pixels and the other consisting of sunlit-only pixels. Using the Artificial Neural Network (ANN) algorithm and PROSAIL model, we developed a hybrid inversion model to assess the performance of the filtered VIs on the two measured datasets. The results demonstrated that TCARI/OSAVI and SR3 were the most accurate VIs for estimating LAI (including shaded pixels: R2 = 0.67, RMSE = 0.31 m2/m2; sunlit-only pixels: R2 = 0.74, RMSE = 0.28 m2/m2) and LCC (including shaded pixels: R2 = 0.70, RMSE = 7.11 μg/cm2; sunlit-only pixels: R2 = 0.73, RMSE = 6.63 μg/cm2) in the two measured reflectance datasets, respectively. Removing canopy shadows significantly improved the accuracy of LAI and LCC retrieval, although there was no significant difference in CCC retrieval accuracy (including shaded pixels: R2 = 0.78, RMSE = 31.25 μg/cm2; sunlit-only pixels: R2 = 0.79, RMSE = 28.48 μg/cm2). Moreover, we utilized UAV imaging multispectral data to map the estimated variability of leaf and canopy traits. The results revealed trait variability among different apple tree canopies, highlighting the potential of UAV imaging multispectral techniques in characterizing and mapping individual apple tree crown traits while capturing variability among crowns. We recommend performing canopy shading pixel masking to enhance the accuracy of ITCs trait retrieval. [ABSTRACT FROM AUTHOR]

Redevelopment of abandoned coal mine industrial sites (ACMISs) has been one of the critical research topics in recent years. The ecological system of ACMISs, which involves environmental, economic, and social characteristics, is an environment-economy-society (EES) composite system. Many factors with conflicting effects are involved in the sustainable development of this system. The matter-element analysis method presented in this paper can transform counteracting problems among influencing factors into compatible problems. This analytical approach leads to a reduction of the uncertainty of these influencing factors and provides a suitable research method to solve the complexity when selecting the most suitable reuse mode of the ACMIS. In this study, eight state-owned ACMISs in the suburbs of Beijing were investigated, and a suitability evaluation index system based on the EES conceptual framework was built. Targeting four reuse modes, including the residential area, the park, the scientific research and office campus, and the business district, we established a matter-element decision-making model to evaluate the priority of reuse mode selections scientifically. The results of our calculations show that the optimal reuse mode for the Qianjuntai, Muchengjian, and Huapogen ACMISs is the park; for the Da'anshan and Changgouyu ACMISs is the business district; for the Datai ACMIS is the scientific research and office campus; and for the Wangping ACMIS is the residential area. Since the suitability level of reusing the Anjiatan ACMIS in any of the four reuse modes is relatively low, it is recommended to reserve it for governmental strategically reserved open space. These real cases demonstrate that the research method is feasible and can be applied to reuse mode decisions for similar abandoned coal mines. [ABSTRACT FROM AUTHOR]