VAR-tree model based spatio-temporal characterization and prediction of O3 concentration in China.

Ozone (O3) pollution in the atmosphere is getting worse in many cities. In order to improve the accuracy of O3 prediction and obtain the spatial distribution of O3 concentration over a continuous period of time, this paper proposes a VAR-XGBoost model based on Vector autoregression (VAR), Kriging method and XGBoost (Extreme Gradient Boosting). China is used as an example and its spatial distribution of O3 is simulated. In this paper, the O3 concentration data of the monitoring sites in China are obtained, and then a spatial prediction method of O3 mass concentration based on the VAR-XGBoost model is established, and finnally its influencing factors are analyzed. This paper concludes that O3 features the highest correlation with PM2.5 and the lowest correlation with SO2. Among the measurement factors, wind speed and temperature are the most important factors affecting O3 pollution, which are positively correlated to O3 pollution. In addition, precipitation is negatively correlated with 8-hour ozone concentration. In this paper, the performance of the VAR-XGBoost model is evaluated based on the ten-fold cross-validation method of sample, site and time, and a comparison with the results of XGBoost, CatBoost (categorical boosting), ExtraTrees, GBDT (gradient boosting decision tree), AdaBoost (adaptive boosting), RF (random forest), Decision tree, and LightGBM (light gradient boosting machine) models is conducted. The result shows that the prediction accuracy of the VAR-XGBoost model is better than other models. The seasonal and annual average R2 reaches 0.94 (spring), 0.93 (summer), 0.92 (autumn), 0.93 (winter), and 0.95 (average from 2016 to 2021). The data show that the applicability of the VAR-XGBoost model in simulating the spatial distribution of O3 concentrations in China performs well. The spatial distribution of O3 concentrations in the Chinese region shows an obvious feature of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The mean concentration is clearly low in winter and high in summer within a season. The results of this study can provide a scientific basis for the prevention and control of regional O3 pollution in China, and can also provide new ideas for the acquisition of data on the spatial distribution of O3 concentrations within cities.

Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016-2021.

Fine particulate matter (PM2.5) has a continuing impact on the environment, climate change and human health. In order to improve the accuracy of PM2.5 estimation and obtain a continuous spatial distribution of PM2.5 concentration, this paper proposes a LUR-GBM model based on land-use regression (LUR), the Kriging method and LightGBM (light gradient boosting machine). Firstly, this study modelled the spatial distribution of PM2.5 in the Chinese region by obtaining PM2.5 concentration data from monitoring stations in the Chinese study region and established a PM2.5 mass concentration estimation method based on the LUR-GBM model by combining data on land use type, meteorology, topography, vegetation index, population density, traffic and pollution sources. Secondly, the performance of the LUR-GBM model was evaluated by a ten-fold cross-validation method based on samples, stations and time. Finally, the results of the model proposed in this paper are compared with those of the back propagation neural network (BPNN), deep neural network (DNN), random forest (RF), XGBoost and LightGBM models. The results show that the prediction accuracy of the LUR-GBM model is better than other models, with the R2 of the model reaching 0.964 (spring), 0.91 (summer), 0.967 (autumn), 0.98 (winter) and 0.976 (average for 2016-2021) for each season and annual average, respectively. It can be seen that the LUR-GBM model has good applicability in simulating the spatial distribution of PM2.5 concentrations in China. The spatial distribution of PM2.5 concentrations in the Chinese region shows a clear characteristic of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The seasonal variation in mean concentration values is marked by low summer and high winter values. The results of this study can provide a scientific basis for the prevention and control of regional PM2.5 pollution in China and can also provide new ideas for the acquisition of data on the spatial distribution of PM2.5 concentrations within cities.

Parent-Child Attachment and Social Adaptation Behavior in Chinese College Students: The Mediating Role of School Bonding.

Family and school are two main places for adolescents to develop socialization, which can be contributed by good parent-child attachment and school bonding. Earlier studies suggested that parent-child attachment played an important role in promoting the formation of high-level school bonding, which is also likely to influence social adaptation. This study aimed to explore the relationship between parent-child attachment and social adaptation, and the mediating role of school bonding. Using stratified cluster sampling, 1,440 college students were first randomly selected from four universities and then stratified by specialty with a balance between genders and grades. Participants voluntarily participated in this study and completed questionnaires including the Parent-Child Attachment Scale, School Bonding Scale, and Social Adaptation Scale. Finally, a total of 1,320 college students were included in the analysis (59.5% female; aged 18-24years, Mage=20.39±1.52years). Data analysis and structural equation modeling were conducted using SPSS 22.0 and AMOS 23.0. The results indicated that the overall level of parent-child attachment in females (M=75.72, SD=12.36) was significantly higher than that of males (M=73.71, SD=12.68; F=8.22, p<0.01). Difference was also found between sibling status (F=13.90, p<0.001), and the only-child (M=76.16, SD=12.72) scored significantly higher than their counterparts (non-only children, M=73.60, SD=12.19). Parent-child attachment was positively correlated with social adaptation (p<0.01) and school bonding (p<0.01), while school bonding was also positively correlated with social adaptation score (p<0.01). School bonding played a partial intermediate role in the relationship between parent-child attachment and social adaptation (ß=0.15). Our research identified a direct influence of parent-child attachment and an indirect influence via school bonding on social adaptation among college students.