[Analysis of the Jumping Characteristics and Influencing Factors of Ozone Pollution in Beijing].

Based on environmental monitoring data and meteorological observation data from 2016 to 2022 in Beijing, combined with backward trajectory clustering and potential source area contribution analysis, the characteristics, meteorological impacts, and potential source areas of ozone (O3) pollution were analyzed. The results showed that there was a total of 41 O3 pollution processes with jumping characteristics in Beijing from 2016 to 2022, with an average of 5.9 times a year. The occurrence time was concentrated in May to July, and the day of the jump (OJD2) was higher than the day before the jump (OJD1). The average value of ρ(O3-8h) was 78.3% higher, and the peak concentration was 78.9% higher. The high O3 concentration zone in the OJD2 region exhibited a characteristic of advancing from south to north. The main reasons for the occurrence of jumped O3 pollution in Beijing could be summarized as local accumulation caused by unfavorable meteorological conditions and regional transmission impact. The occurrence of jump-type ozone pollution was characterized by an increase in southerly wind frequency, temperature rise, pressure decrease, and precipitation decrease. The increase in southerly wind frequency provided conditions for the transport of O3 and its precursors, and rapid photochemical reactions occurred under local high temperatures, with less superimposed precipitation, comprehensively pushing up the ozone concentration level of OJD2. Six air mass transporting pathways were identified through clustering analysis; the air mass from the direction north of OJD2 decreased by 11.2%, whereas the air mass from the south and east directions increased by 6.7% and 4.4%, respectively, with the air masses mainly transmitting over short distances. The ozone concentration corresponding to the south and east directions was relatively high, making a significant contribution to Beijing's pollution. The analysis of potential source areas revealed that the main potential source areas of OJD2 ozone pollution were the central, southern, and eastern parts of Beijing-Tianjin-Hebei, which contributed 82.6% to the pollution trajectory. There was a significant contribution of regional transport during jump-type ozone pollution, and it is necessary to strengthen joint prevention and control in the Beijing-Tianjin-Hebei Region.

A Novel ABRM Model for Predicting Coal Moisture Content.

Coal moisture content monitoring plays an important role in carbon reduction and clean energy decisions of coal transportation-storage aspects. Traditional coal moisture content detection mechanisms rely heavily on detection equipment, which can be expensive or difficult to deploy under field conditions. To achieve fast prediction of coal moisture content, a novel neural network model based on attention mechanism and bidirectional ResNet-LSTM structure (ABRM) is proposed in this paper. The prediction of coal moisture content is achieved by training the model to learn the relationship between changes of coal moisture content and meteorological conditions. The experimental results show that the proposed method has superior performance in terms of moisture content prediction accuracy compared with other state-of-the-art methods, and that ABRM model approaches appear to have the greatest potential for predicting coal moisture content shifts in the face of meteorological elements.

[Three-dimensional Structure Variation of PM2.5 During Cold Front Advance in Eastern China].

Based on observational data and the WRF-Chem model, this study analyzed the large-scale air pollution in eastern China, which was caused by the weather process of a cold front moving southward, emphasizing the vertical structure of the boundary layer and the influence on the three-dimensional structure of PM2.5. Our observations revealed that the heavy pollution near the surface was located at sparse or equal isobar in front of the cold front. During the process of the weather system moving in, the timing of the maximum PM2.5 concentration at each station from north to south was delayed. The results show that the WRF-Chem model can better capture the spatial and temporal variations of surface and upper air meteorological elements and PM2.5 concentrations over eastern China. The simulation results show that the boundary layer structure and the vertical profile of PM2.5 at the same location of the mobile weather system showed similar characteristics. When the invasion takes place in a cold front, the pollutants in front of the cold front are rapidly lifted from the ground to a high altitude. The growth in PM2.5 concentration and the increasing wind speed at high altitudes lead to the upward trend in PM2.5 flux. As the altitude increases, the high-value area of PM2.5 concentration tilts towards the warm air mass. The transit of the cold front at night led to more unstable convection within the boundary layer; the height of the boundary layer increased from north to south, reaching over 1 km, breaking the rules characteristic of the diurnal evolution of the boundary layer. The results indicate that the combination of vertical observations and elaborate simulation can effectively explain the impact of synoptic processes on the transport, distribution, and evolution of air pollution and provide precise pollution-control directives.

[Characteristics of Atmospheric Particulate Matter Pollution and the Unique Wind and Underlying Surface Impact in the Twain-Hu Basin in Winter].

To understand the recent characteristics of atmospheric environmental changes in the Twain-Hu(Hunan-Hubei) Basin, including the middle reaches of the Yangtze River, this paper uses near-surface PM2.5 and PM10 observational data for the Twain-Hu Basin in the winters of 2015 to 2019, combined with wind-speed, topography, the normalized difference vegetation index(NDVI), and other datasets. The results show that:① PM2.5 pollution occurred frequently in the winters of 2015-2019 in the Twain-Hu Basin, and Xiangyang and Jingmen in the western part of the basin, experience PM2.5 pollution on an average of 62 and 61 days in winter(PM2.5>75 µg·m-3). And the heavy pollution days in Xiangyang reached 19 more days(PM2.5>150 µg·m-3), indicating that the Twain-Hu Basin is an air pollution center in the middle reaches of the Yangtze River Basin; ② Spatially, pollution in the Twain-Hu Basin is heavier in the northwest than in the southeast, and in the urban agglomeration, which is mainly related to the regional transport of air pollutants by the winter monsoon and the high levels of emissions from urban areas; ③ A "U-shaped" nonlinear relationship was observed between near-surface wind speeds and PM2.5 and PM10 concentrations. The inflection points of PM2.5 and PM10 concentrations were 153 and 210 µg·m-3, respectively. This implies that the accumulation of local atmospheric particulate matter in the Twain-Hu Basin dominates light/medium pollution, while the regional transport of air pollutants dominates period of severe pollution; and ④ PM2.5 and PM10 in winter were significantly negatively correlated with terrain height and the NDVI, which reflects the atmospheric environmental effects of topography and urbanization.

[Analysis of the Continuous Heavy Pollution Process in the Winter of 2016 in Beijing, Tianjin, and Hebei].

From December 16 to 21, 2016, continuous heavy haze occurred in the Beijing-Tianjin-Hebei region of China. To further understand the development mechanism of haze and improve the forecasting of haze events, a variety of data, background circulation, and the meteorological elements of this severe haze process were comprehensively analyzed. The results show that the process lasted for a long time, pollution intensity was high, influence area was wide, visibility was low, and external transport was the main cause. Aerosols were mainly distributed at a height of 600 m, and there was a certain extreme anomaly. The static weather index and air quality index showed a strong correlation. The air in the Beijing-Tianjin-Hebei region is controlled by zonal circulation in front of a high-pressure ridge, which maintains the westerly airflow. Cold-air activity is weak, and downflow is dominant. The water vapor content is low, the cloud cover is limited at high altitudes, and a warm ridge extends northwards at low altitudes. Limited static wind on the ground, high relative humidity, and low mixing-layer height are not conducive to the horizontal and vertical diffusion of pollutants.

Temporal and spatial variations of meteorological elements and reference crop evapotranspiration in Alpine regions of Tibet, China.

Reference evapotranspiration (ET0) is an important parameter for characterization of the hydrological cycle, and it is also important for agricultural, environmental, and other studies. In this study, by collecting the daily meteorological data of 31 base stations in high-altitude areas of Tibet for 35 consecutive years, the daily reference crop evapotranspiration (ET0) of each base station is calculated by the ASCE Penman-Monteith formula. The Mann-Kendall method is applied primarily to test the trend of meteorological elements and ET0. In addition, the software ArcGIS was used to generate spatial interpolation distribution maps of meteorological elements and ET0 so that the spatio-temporal variation trend of each base station is carried out. The ET0 in Shigatse shows an insignificant downward trend, while the other six regions show an upward trend, which is related to temperature and humidity directly with R2 of 0.23 and 0.67. The relative humidity (RH) and sunshine duration (SD) in Tibet show an overall three-step distribution in the east, middle, and west with RH decreasing from east to west and SD increasing from east to west. Annual cumulative ET0 of Ngari is the largest and decreases from west to east gradually, while ET0 increasing from east (lower elevation) to west (higher elevation) of Tibet with ranging from 730 to 1255 mm/year. This study not only is important for understanding of ET0 changes but also provides the preliminary and elementary reference for agricultural water management in Tibet with high elevation.

[Source Apportionment and Optical Properties of Fine Particles Associated with Regional Pollution in the Yangtze River Delta].

In order to study the distribution and optical characteristics of fine particulate matter pollution in the Yangtze River Delta, PM2.5 samples were collected and analyzed from city (Suzhou), suburb (Nanjing), and regional background monitoring stations (Lin'an). The average concentrations of PM2.5 in Suzhou, Nanjing, and Lin'an were (169.8±56.5), (169.9±51.2), and (154.0±54.9) µg·m-3, respectively. There was little difference in meteorological conditions and the chemical composition of PM2.5 among the three sites, and PM2.5 pollution showed significant synchronization and regionalization characteristics. The extinction coefficients estimated using a chemical component method for Suzhou, Nanjing, and Lin'an are (561±223), (655±340), and (679±349) Mm-1, respectively. There is strong correlation between the extinction coefficients estimated by the chemical component method and those estimated by a visibility-based method (r 0.73-0.80). Using a PMF model to analyze the PM2.5 sources, secondary nitrate sources (32%) and secondary sulfate sources (25%) accounted for the largest proportions followed by biomass combustion (16%), incomplete combustion (7%), fuel combustion (7%), soil crusts (8%), and marine sources (5%). The primary sources of the extinction coefficients of PM2.5 are secondary nitrate and sulfate sources, incomplete combustion, and biomass combustion. Compared with the source contribution of mass concentrations, the proportion of secondary nitrate and sulfate sources decreased by approximately 4% and the proportion of the incomplete combustion source increased by 5%. These results show that there are differences in the contributions of various PM2.5 source according to mass concentrations and extinction coefficients.

Annual variations of black carbon over the Yangtze River Delta from 2015 to 2018.

In this study, the black carbon (BC) measurements in the atmosphere of Nanjing, China were continuously conducted from 2015 to 2018 using a Model AE-33 aethalometer. By combining dataset of PM2.5, PM10, CO, NO2, SO2, O3 and meteorological parameters, the temporal variations and the source apportionment of BC were given in this study. The results showed that the PM2.5 mass concentrations decreased in Nanjing, with an average annual rate of variation of 6.50 µg/(m3⋅year). Differently, the annual average concentrations of BC increased with an average annual variation rate of 214.71 ng/(m3⋅year). The seasonal variations showed the pattern of BC mass concentrations in winter > autumn > spring > summer. The diurnal variations of BC mass concentrations showed a double-peak in all four seasons. The first peak occurred at approximately 7:00 in spring, summer and autumn and around 8:00 in winter. The second peak took place after 18:00. The average AAE (absorption Ångström exponent) was 1.26 with a maximum of 1.35 during wintertime and the lowest (1.12) during summertime. In addition, the AAE was smaller in the daytime than that at night, with a minimum occurring between 13:00 and 14:00. BC and visibility show a good power-function relationship at different humidity levels. The average values of the visibility thresholds of the BC mass concentrations in spring, summer, autumn and winter were 1.326, 5.522, 1.340 and 0.708 µg/m3, respectively. The greater the relative humidity, the smaller the visibility threshold for the BC mass concentrations was.

[Concentration Characteristics of PM2.5 and the Causes of Heavy Air Pollution Events in Beijing During Autumn and Winter].

To study the changing of characteristics and formation mechanisms of PM2.5 in Beijing during the last two years, particulate matter concentrations, weather conditions, and air-mass trajectories were analyzed during severe pollution episodes in fall and winter 2016-2017 using routine observations and the TrajStat model. Results showed that 13 heavy pollution events, each lasting at least two days, occurred in Beijing. Of these, approximately 61.5% occurred in winter, characterized by heavier pollution concentrations and longer durations than those occurring in autumn. A low-pressure gradient, high humidity, low surface wind speed, low boundary layer, and particular terrain (i. e., being surrounded by mountains on three sides) all contributed to the high occurrence frequency of severe pollution episodes in autumn and winter. During the pollution episodes, the average ratio of PM2.5 to PM10 reached 0.86. The air-masses during the accumulation stage were mainly transported from the northwest, west, southwest, and southeast of Beijing. The southwestern and southeastern transmission paths accounted for 21.6% of the total pollution load. In addition, the WRF-CAMx model was used to quantitatively analyze the contributions of local and external sources to the concentrations of PM2.5 in Beijing during 16-22 December 2016. Based on this analysis, PM2.5 contributions notably varied with different air-masses; in the case of southern air-masses, external sources dominated the PM2.5 concentrations in Beijing and local contributions decreased rapidly; in contrast, in the case of northwestern air-masses, the opposite pattern occurred. Overall, the contribution of local sources to PM2.5 concentrations in Beijing varied from 16.5% to 69.3% during the monitored pollution episodes.

Study on the relationship between meteorological factors and the number of hypertension outpatients based on CNN-LSTM / 中华疾病控制杂志

Objective To study the effect of meteorological factors on the number of hypertension outpatients in four areas of Gansu Province, then predict and analyze the trend of the number of hypertension outpatients, so as to provide reference for the prevention and control of hypertension diseases. Methods On the basis of controlling the confounding factors such as long-term trends, date effects, meteorological information and contaminant influence, a mixed model of convolutional neural network (CNN) and long-short term memory (LSTM) was constructed for the number of hypertension outpatients in the four regions of Baiyin, Chengxian, Qingcheng and Liangzhou by Python programming language. Results The root mean square errors of the CNN-LSTM model for the number of hypertensive outpatients in the four regions was 6.330 9, 6.814 2, 6.393 6 and 6.867 6. The mean absolute percentage error was 74.082 2, 78.508 2, 56.618 3 and 50.235 4. And the average absolute errors was 4.875 7, 5.431 1, 4.542 0 and 6.460 8. All the results was superior to those of support vector machine (SVM), autoregressive integrated moving average model (ARIMA), random forest (RF), CNN and LSTM. Conclusion The CNN-LSTM model can accurately predict the number of hypertension outpatients in Gansu. The hospital can rationally allocate medical resources according to the needs of hypertension for medical treatment at different times.

The Temporal and Spatial Distribution Characteristics of Air Pollution Index and Meteorological Elements in Beijing, Tianjin, and Shijiazhuang, China.

With rapid economic development and continuous population growth, several important cities in China suffer serious air pollution, especially in the Beijing-Tianjin-Hebei economic developing area. Based on the daily air pollution index (API) and surface meteorological elements in Beijing, Tianjin, and Shijiazhuang (the capital of Hebei province) from 2001 to 2010, the relationships between API and meteorological elements were analyzed. The statistical analysis focused on the relationships at seasonal and monthly average scales, on different air pollution grades and air pollution processes. The results revealed that the air pollution conditions in the 3 areas gradually improved from 2001 to 2010, especially during summer; the worst conditions in air quality were recorded in Beijing in spring due to the influences of dust, and in Tianjin and Shijiazhuang in winter due to household heating. Meteorological elements exhibited different influences on air pollution, showing similar relationships between API in monthly averages and 4 meteorological elements (i.e., the average, maximum, and minimum temperatures; maximum air pressure; vapor pressure; and maximum wind speed), whereas the relationships on a seasonal average scale demonstrated significant differences. Compared with seasonal and monthly average scales of API, the relation coefficients based on different air pollution grades were significantly lower, whereas the relationship between API and meteorological elements based on air pollution processes reduced the smoothing effect due to the average processing of seasonal and monthly API and improved the accuracy of the results. Finally, statistical analysis of the distribution of pollution days in different wind directions indicated the directions of extreme and maximum wind speeds that mainly influence air pollution, representing valuable information that could support the definition of air pollution control strategies through the identification of the regions (and the located emission sources) where the implementation of emission reduction actions should be focused. Integr Environ Assess Manag 2018;14:710-721. © 2018 SETAC.