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In recent years, the concentrations of PM2.5 in urban ambient air in China have been declining; however, the strong atmospheric oxidation capacity (AOC) represents challenges to the further reduction of PM2.5 concentration and the continuous improvement of ambient air quality in China in the future, since the overall AOC is still at a high level. For this paper, based on ground observation data recorded in Beijing from 2016 to 2019, the variation in AOC was characterized according to the concentration of odd oxygen (OX = O3 + NO2). The concentrations of the primary and secondary components of PM2.5 were analyzed using empirical formulas, the correlation between AOC and the concentrations of secondary PM2.5 and the secondary inorganic components (SO42−, NO3−, NH4+, and SNA) in Beijing were explored, the impact of atmospheric photochemical reaction activity on the generation of atmospheric secondary particles was evaluated, and the impact of atmospheric oxidation variations on PM2.5 concentrations and SNA in Beijing was investigated. The results revealed that OX concentrations reached their peak in 2016 and reached their lowest point in 2019. The OX concentrations followed a descending seasonal trend of summer, spring, autumn, and winter, along with a spatial descending trend from urban observation stations to suburban stations and background stations. The degree of photochemical activity and the magnitude of the AOC have a large influence on the production of atmospheric secondary particles. When the photochemical reaction was more active and the AOC was stronger, the mass concentrations of the secondary generated PM2.5 fraction were higher and accounted for a higher proportion of the total PM2.5 mass concentrations. In the PM2.5 fraction, SNA accounted for 50.7% to 94.4% of the total mass concentrations of water-soluble inorganic ions in the field observations. Higher concentrations of the atmospheric oxidant OX in ambient air corresponded to a higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), suggesting that the increase in AOC could promote the increase of PM2.5 concentration. Based on a relationship analysis of SOR, NOR, and OX, it was inferred that the relationship between OX and SOR and the relationship between OX and NOR were both nonlinear. Therefore, when establishing PM2.5 control strategies in Beijing in the future, the impact of the AOC on PM2.5 generation should be fully considered, and favorable measures should be taken to properly regulate the AOC, which would be more effective when carrying out further control measures regarding PM2.5 pollution. [ABSTRACT FROM AUTHOR]

China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development. [ABSTRACT FROM AUTHOR]

Aerosol Optical Depth (AOD) is one of the most important parameters of aerosol and a key physical quantity to characterize atmospheric turbidity and air pollution. Accurate retrieval of AOD is of great significance for air quality assessment. However, the spatial resolution of the currently widely used Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products is too low to meet the application research of atmospheric environment at the regional scale. In 2013, China launched the Gaofen-1 (GF-1) satellite, which provides a new idea for AOD retrieval. In this paper, we apply the synergetic use of TERRA and AQUA satellite MODIS data to calculate the high-resolution AOD over Beijing based on the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) and discussed scale conversion problems between AODs with different resolutions. To obtain the 100 m MODIS data, we use GF-1 wide-field-of-view data to downscale 1 km MODIS data based on mutual information method. The retrieved AOD has a spatial resolution of 100 m and can cover many land surface types. Preliminary validation was carried out with the Aerosol Robotic Network (AERONET) ground observation data. The correlation coefficient is about 0.88, and the root-mean-square error is about 0.15. Due to the high resolution of retrieved results, more detailed features can be provided in the spatial distribution. The experimental results show that the method has high precision, and further verification work is continuing. [ABSTRACT FROM AUTHOR]

Most studies on Chinese dormitories are carried out in summer, while few focus on a transition season or winter. This study evaluated the air quality of a student dormitory in a university in the Beijing area by using a questionnaire survey and on-site measurements. The CO2 concentration was used as an indoor air quality evaluation index to characterize the freshness of the air, and different window opening conditions in the dormitory were simulated, with corresponding improvement plans proposed. The results of this study revealed that the air quality and thermal comfort of the student dormitories during a transition season and winter fell short of expectations. According to the survey, students who opened their windows frequently had a better subjective perception of the air quality. However, due to the large temperature difference between day and night, more than 80% of the students felt too cold when opening the windows. For daytime conditions, the area of unilateral ventilation window opening should not be less than 0.39 m2, the area of bilateral ventilation window opening should not be less than 0.13 m2, and the time taken to close the windows and doors should not exceed the maximum ventilation interval. Empirical equations were fitted for nighttime conditions based on the CO2 concentration, number of people in the room, and window opening area, resulting in a reasonable window opening area of 0.349 m2~0.457 m2. In sum, this study assessed the air quality status within typical university dormitories across varying seasons, gaining a clear understanding of how different ventilation strategies and occupant densities influence air freshness and thermal comfort. Based on these insights, a practical and optimized window area recommendation was formulated to enhance the indoor environmental quality in these dormitories. [ABSTRACT FROM AUTHOR]

Black carbon (BC), an important component of atmospheric aerosol, plays a significant role in regional climate, hydrological cycle, variation of monsoon rainfall, and human health. The 40-year detailed atmospheric BC over China from 1981 to 2020 is systematically investigated through the MERRA-2 reanalysis dataset. MERRA-2 BC generally showed a good correlation (R = 0.68) compared with 673 monthly samples from ground-based observation at 35 stations around China. The overall annual average of MERRA-2 BC concentration over China is 1.15 μg/m3, with a fast growth rate during 1981–2007 and a relatively slow decrease after that. The winter season has the highest mean concentration of BC, followed by autumn and spring, whereas summer shows relatively weaker values. The order of annual average BC concentrations during 1981–2020 is Beijing-Tianjin-Hebei region (BTH, 4.02 μg/m3) > Sichuan Basin (SB, 3.94 μg/m3) > Yangtze River Delta (YRD, 2.68 μg/m3) > Pearl River Delta (PRD, 1.47 μg/m3). The monthly mean BC concentrations over the BTH, YRD, PRD and SB are estimated to be smallest 3.18 μg/m3 in May, 1.94 μg/m3 in August, 0.82 μg/m3 in July, 3.04 μg/m3 in June, respectively, whilst largest consistently in December with 5.09 μg/m3, 3.83 μg/m3, 2.12 μg/m3, and 5.41 μg/m3, respectively. Our study indicates the primary potential source areas for BC are concentrated in the research city and its surroundings. Beijing and Chengdu are more BC-polluted areas than Shanghai and Guangzhou. Long-distance, regional transfer from south BTH contributes importantly to BC pollution in Beijing under the influence of prevailing southerly winds. The geographical location of Chengdu causes the transport and accumulation of BC inside the SB. [ABSTRACT FROM AUTHOR]

For curbing the global climate crisis, China has set an ambitious target of peak carbon emissions by 2030. Beijing, the capital of China, has implemented a carbon reduction policy since 2012. Using the reduced and generalized forms of the Environmental Kuznets Curve (EKC), we deduce that both the cubic EKC and the genetic algorithm-based EKC have an N-shape. The first turning point of the three-order EKC occurs around 2011, demonstrating the effectiveness of the carbon reduction policy. However, the time series model predicts that Beijing will reach the second turning point around 2026, when the gross domestic product (GDP) is about CNY 5000 billion and carbon emissions will begin to increase again. Interpretable machine learning is proposed to explore the socio-economic drivers in carbon emissions, indicating that total energy consumption and GDP contribute the most. Therefore, we should accelerate the upgrading of energy consumption and adjust the industrial structure, thus facilitating Beijing to its peak carbon emissions and achieving carbon neutrality. [ABSTRACT FROM AUTHOR]

There is an important significance for human health in predicting atmospheric concentration precisely. However, due to the complexity and influence of contingency, atmospheric concentration prediction is a challenging topic. In this paper, we propose a novel hybrid learning method to make point and interval predictions of PM2.5 concentration simultaneously. Firstly, we optimize Sparrow Search Algorithm (SSA) by opposition-based learning, fitness-based learning, and Lévy flight. The experiments show that the improved Sparrow Search Algorithm (FOSSA) outperforms SSA-based algorithms. In addition, the improved Sparrow Search Algorithm (FOSSA) is employed to optimize the initial weights of probabilistic forecasting model with autoregressive recurrent network (DeepAR). Then, the FOSSA–DeepAR learning method is utilized to achieve the point prediction and interval prediction of PM2.5 concentration in Beijing, China. The performance of FOSSA–DeepAR is compared with other hybrid models and a single DeepAR model. Furthermore, hourly data of PM2.5 and O3 concentration in Taian of China, O3 concentration in Beijing, China are used to verify the effectiveness and robustness of the proposed FOSSA–DeepAR learning method. Finally, the empirical results illustrate that the proposed FOSSA–DeepAR learning model can achieve more efficient and accurate predictions in both interval and point prediction. [ABSTRACT FROM AUTHOR]

The values of ratio a of the linear relationship between specific attenuation and specific differential phase vary significantly in convective storms as a result of resonance scattering. The best-linear-fit ratio a at X band is determined using the modified attenuation correction algorithm based on differential phase and attenuation, as well as the premise that reflectivity is unattenuated in S band radar detection. Meanwhile, the systemic reflectivity bias between the X band radar and S band radar and water layer attenuation (ZW) on the wet antenna cover of the X band radar are also considered. The good performance of the modified correction algorithm is demonstrated in a moderate rainfall event. The data were collected by four X band dual-polarization (X-POL) radar sites, namely, BJXCP, BJXFS, BJXSY, and BJXTZ, and a China's New Generation Weather Radar (CINRAD/SA radar) site, BJSDX, in Beijing on 20 July 2016. Ratio a is calculated for each volume scan of the X band radar, with a mean value of 0.26 dB deg−1 varying from 0.20 to 0.31 dB deg−1. The average values of systemic reflectivity bias between the X band radar (at BJXCP, BJXFS, BJXSY, and BJXTZ) and S band radar (at BJSDX) are 0, −3, 2, and 0 dB, respectively. The experimentally determined ZW is in substantial agreement with the theoretically calculated ones, and their values are an order of magnitude smaller than rain attenuation. The comparison of the modified attenuation correction algorithm and the empirical-fixed-ratio correction algorithm is further evaluated at the X-POL radar. It is shown that the modified attenuation correction algorithm in the present paper provides higher correction accuracy for rain attenuation than the empirical-fixed-ratio correction algorithm. [ABSTRACT FROM AUTHOR]

Greenhouse vegetable production in China not only increases farmers' income, but also increases the risk of nitrogen losses due to excessive water and fertilizer input. Nitrogen losses, including the potent greenhouse gas nitrous oxide (N2O), are driven by water content, soil temperature and pH; regulated by available organic carbon and inorganic nitrogen (N); and affected by management. Therefore, a process-based model was applied to explain the complex interaction of the factors affecting N losses in the form of N2O, NH3 and NO3− from a greenhouse vegetable production system in a northeast suburb of Beijing, China. We designed four treatments: two equal N input treatments with one flooding (FP) and the other drip irrigation (FPD) and two equal water input treatments (drip irrigation) with one 100% chemical N input (FPD) and the other 50% N input (OPTD). The last one was CK treatment (flooding without chemical N). We calibrated the WHCNS-veg model using year-round measurements of soil temperature, N2O emission, NH3volatilization, NO3− distribution and yields for greenhouse cucumber–tomato cultivation under farmers' practice (flooding + 100% chemical N, FP). Then, we validated the model using the data sets under drip irrigation (70% of flooding amount + 100% chemical N, FPD), reduced chemical N by 50% (drip + 50% chemical N, OPTD) and CK treatment. The WHCNS-veg model was able to capture the above processes under different treatments. Annual N2O emissions were 5.47 and 3.76kg N ha−1 for the cucumber and tomato seasons under FP, respectively. Compared to FP, drip irrigation (FPD) decreased N2O emissions by 19.0% and 45.5% in the two seasons, respectively. Compared to FPD, applying a lower rate of N (OPTD) further reduced N2O emissions by 13.7% and 40.5%, respectively. According to the model simulation, N2O emission was mainly controlled by nitrification/denitrification in the cucumber/tomato seasons, respectively. Compared to FP, drip irrigation (FPD) increased NH3 volatilization by 54.2% in the cucumber season, while in the tomato season, there were no significant differences inNH3 volatilization under the three fertilizer treatments. The nitrate leaching levels were 48.5 and 81.0 kg N ha−1 for the two seasons under FP treatment. Drip irrigation (FPD) decreased NO3− leaching by 20.6% in the cucumber season. Drip irrigation (FPD) and/or reducing chemical N (OPTD) did not compromise vegetable yields. In all, WHCNS-veg performed well in simulating N2O, NH3 and NO3− dynamics from the greenhouse vegetable field, which means that the model can be used to manage water and nitrogen precisely in greenhouse vegetable production systems by scenario analysis, and drip irrigation and/or lower N input can be applied in this area to secure yield and reduce N losses. [ABSTRACT FROM AUTHOR]

Organic components in atmospheric fine particulate matter have attracted much attention and several scientific studies have been performed, although most of the sample extraction methods are time consuming and laborious. Accelerated solvent extraction (ASE) is a new sample extraction method offering number of advantages, such as low extraction cost, reduced solvent and time consumption, and simplified extraction protocols. In order to optimize ASE methods to determine the concentrations of organic compounds in atmospheric fine particulate matter, different parameters were set out for the experiment, and the optimal method was selected according to the recoveries of the standard (i.e., n−alkanes and polycyclic aromatic hydrocarbons (PAHs)). This study also involves a comparison of the optimal method with the traditional method of ultrasonic extraction (USE). In addition, the optimized method was applied to measure the mass concentrations of organic compounds (n−alkanes and PAHs) in fine particulate matter samples collected in Beijing. The findings showed that the average recovery of target compounds using ASE was 96%, with the majority of compounds falling within the confidence levels, and the ASE recoveries and precision were consistent with the USE method tested. Furthermore, ASE combines the advantages of high extraction efficiency, automation, and reduced solvent use. In conclusion, the optimal ASE methods can be used to extract organic components in atmospheric particulate matter and serve as a point of reference for the development of analytical methodologies for assessing organic compounds in atmospheric particulate matter in China. [ABSTRACT FROM AUTHOR]

In recent years, frequent severe haze weather has formed in China, including some of the most populated areas. We found that these smog-prone areas are often relatively a "local climate" and aim to explore this series of scientific problems. This paper uses remote sensing and data mining methods to study the correlation between haze weather and local climate. First, we select Beijing, China and its surrounding areas (East longitude 115°20′11″–117°40′35″, North latitude 39°21′11″–41°7′51″) as the study area. We collected data from meteorological stations in Beijing and Xianghe from March 2014 to February 2015, and analyzed the meteorological parameters through correlation analysis and a grey correlation model. We study the correlation between the six influencing factors of temperature, dew point, humidity, wind speed, air pressure and visibility and PM2.5, so as to analyze the correlation between haze weather and local climate more comprehensively. The results show that the influence of each index on PM2.5 in descending order is air pressure, wind speed, humidity, dew point, temperature and visibility. The qualitative analysis results confirm each other. Among them, air pressure (correlation 0.771) has the greatest impact on haze weather, and visibility (correlation 0.511) is the weakest. [ABSTRACT FROM AUTHOR]

To investigate the evolution of the nocturnal boundary layer (NBL) and its impacts on the vertical distributions of pollutant particulates, a combination of in situ observations from a large tethered balloon, remote sensing instruments (aerosol lidar and Doppler wind lidar) and an atmospheric environment-monitoring vehicle were utilized. The observation site was approximately 100 km southwest of Beijing, the capital of China. Results show that a considerable proportion of pollutant particulates were still suspended in the residual layer (RL) (e.g., the nitrate concentration reached 30 μg m−3) after sunset. The NBL height calculated by the aerosol lidar was closer to the top of the RL before midnight because of the pollutants stored aloft in the RL and the shallow surface inversion layer; after midnight, the NBL height was more consistent with the top of the surface inversion layer. As the convective mixing layer gradually became established after sunrise the following day, the pollutants stored in the nocturnal RL of the preceding night were entrained downward into the mixing layer. The early morning PM2.5 concentration near 700 m in the RL on 20 December decreased by 83% compared with the concentration at 13:34 on 20 December at the same height. The nitrate concentration also decreased significantly in the RL, and the mixing down of nitrate from the RL could contribute about 37% to the nitrate in the mixing layer. Turbulence activities still existed in the RL with the bulk Richardson number (Rb) below the threshold value. The corresponding increase in PM2.5 was likely to be correlated with the weak turbulence in the RL in the early morning. [ABSTRACT FROM AUTHOR]

Based on 139 nights of observational data of the Rayleigh lidar site in Beijing, China (40.5° N, 116.2° E), typical lower MIL cases and their temperature inversion evolution process were reported and compared with the SABER data from the same time. Meanwhile, the seasonal distribution of lower MIL cases over North China was also statistically analyzed. The average inversion temperature of the low MIL is 23.4 K, and the average layer thickness is 4.78 km with an average MIL bottom altitude of 68.2 km. Meanwhile, 65% of the MIL propagates vertically, most of which goes downward. These results show the temperature behavior properties of the lower MIL over North China, which may be helpful for us to further understand middle atmosphere chemical and dynamics processes. [ABSTRACT FROM AUTHOR]

Based on application of the atomic filter technology in a signal detection system of lidar, the diurnal observation of sodium lidar were obtained using the system at the National Space Science Center of the Chinese Academy of Sciences at Beijing Yanqing station (40.5° N, 116° E) in April 2014. During the lidar observation period, among the 103 cases of continuous daytime observations, the longest time was 181 h. In the case of a continuous observation period of 5 days (13–18 October 2014), the signal-to-noise ratio reached to 19:1 at 12:00–13:00 Local Time of the daytime, when the spatial and time resolutions were respectively set to 96 m of 167 s. The improvements resulted in the highest detection level of any existing diurnal lidars in China. Some interesting phenomena such as the sporadic sodium layer have also been observed during the daytime. The daytime capability extended the observing time range of the earlier systems that were limited to only nighttime observations. This innovation provides a useful method for the studies of diurnal tides, photochemistry, gravity waves, and correlative modeling studies. [ABSTRACT FROM AUTHOR]

In this study, using Aerosol Robotic Network aerosol optical depth (AOD) products at three stations in the North China Plain (NCP)—a heavily polluted region in China—the AOD products from six satellite-borne radiometers: the Moderate Resolution Imagining Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR), Ozone Mapping Imaging (OMI), the Visible Infrared Imaging Radiometer (VIIRS), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Polarization and Directionality of the Earth's Reflectances (POLDER), were thoroughly validated, shedding new light on their advantages and disadvantages. The MODIS Deep Blue (DB) products provide more accurate retrievals than the MODIS Dark Target (DT) and other satellite products at the Beijing site (BJ, a megacity), with higher correlations with AERONET (R > 0.93), lower mean absolute bias (MB < 0.012), and higher percentages (>68%) falling within the expected error (EE). All MODIS DT and DB products perform better than the other satellite products at the Xianghe site (XH, a suburb). The MODIS/Aqua DT products at both 3-km and 10-km resolutions performed better than the other space-borne AOD products at the Xinglong site (XL, a rural area at the top of a mountain). MISR, VIIRS, and SeaWiFS tend to underestimate high AOD values and overestimate AOD values under very low AOD conditions in the NCP. Both OMI and POLDER significantly underestimate the AOD. In terms of data volume, MISR with the limited swath width of 380 km has less data volume than the other satellite sensors. MODIS products have the highest sampling rate, especially the MODIS DT and DB merged products, and can be used for various climate study and air-quality monitoring. [ABSTRACT FROM AUTHOR]

This study demonstrates the capability of a cloud model in simulating a real-world tornado using observed radiosonde data that define a homogeneous background. A reasonable simulation of a tornado event in Beijing, China, on 21 July 2012 is obtained. The simulation reveals the evolution of a descending reflectivity core (DRC) that has commonalities with radar observations, which retracts upward right before tornadogenesis. Tornadogenesis can be divided into three steps: the downward development of mesocyclone vortex, the upward development of tornado vortex, and the eventual downward development of condensation funnel cloud. This bottom-up development provides a numerical evidence for the growing support for a bottom-up, rapid tornadogenesis process as revealed by the state-of-the-art mobile X-band phase-array radar observations. The evolution of the simulated tornado features two replacement processes of three near-surface vortices coupled with the same midlevel updraft. The first replacement occurs during the intensification of the tornado before its maturity. The second replacement occurs during the tornado's demise, when the connection between the midlevel mesocyclone and the near-surface vortex is cut off by a strong downdraft. This work shows the potential of idealized tornado simulations and three-dimensional illustrations in investigating the spiral nature and evolution of tornadoes. [ABSTRACT FROM AUTHOR]

Concentrated animal feeding operations (CAFOs) such as dairy farms are a source of odorous compound emissions. In this study, by identifying relevant odour sources within a 300-head dairy farm and quantifying their emissions, we determined the separation distances to avoid odour annoyance around the dairy farm with two empirical models (Austrian and German Verein Deutscher Ingenieure (VDI) model) and a dispersion model (AERMOD). Besides, this study ponders on the selection of an appropriate meteorological station that best represents the area surrounding the farm. Results show that the maximum separation distances of an exceedance probability of P = 15% determined by the two empirical and the dispersion models are 524 m, 440 m and 655 m, while the minimum values are 202 m, 135 m, and 149 m, respectively. The NE–SW stretching separation distances match well with the wind rose. The mean ratios of separation distances determined by the two empirical models to that of the dispersion model are 1.23 and 0.95. Moreover, statistics of the separation distances indicate good accordance between the empirical models and the dispersion model. [ABSTRACT FROM AUTHOR]