A New Combination Model for Air Pollutant Concentration Prediction: A Case Study of Xi’an, China

As energy demand continues to increase, the environmental pollution problem is becoming more severe. Governments and researchers have made great efforts to avoid and reduce air pollution. The prediction of PM2.5, as an important index affecting air quality, has great significance. However, PM2.5 concentration has a complex change process that makes its prediction challenging. By calculating both PM2.5 concentration and that of other pollutants in the atmosphere and meteorological factors, it is evident that the variation in PM2.5 concentration is influenced by multiple factors, and that relevant features also influence each other. To reduce the calculated loss, with full consideration given to the influencing factors, we used the maximum correlation and minimum redundancy (MRMR) algorithm to calculate the correlation and redundancy between features. In addition, it is known from the Brock–Dechert–Scheinman (BDS) statistical results that the change in PM2.5 is nonlinear. Due to the outstanding performance of bidirectional long short-term memory (BiLSTM) neural networks in nonlinear prediction, we constructed an encoder–decoder model based on BiLSTM, named ED-BiLSTM, to predict the PM2.5 concentration at monitoring stations. For areas without monitoring sites, due to the lack of historical data, the application of neural networks is limited. To obtain the pollutant concentration distribution in the study area, we divided the study area into a 1 km × 1 km grid and combined the ED-BiLSTM model via the use of the inverse distance weighting (IDW) algorithm to obtain the PM2.5 concentration values in a region without monitoring stations. Finally, ArcGIS was used to visualize the results. The data for the case study were obtained from Xi’an. The results show that, compared with the standard long short-term memory (LSTM) model, the RMSE, MAE, and MAPE of our proposed model were reduced by 24.06%, 24.93%, and 22.9%, respectively. The proposed model has a low error for PM2.5 prediction and can provide a theoretical basis for the formulation of environmental protection policies

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

Simulating the Evolution Mechanism of Inner Innovation in Large-Scale Construction Enterprise with an Improved NK Model

The aim of this study is to simulate the self-organized evolution mechanism of inner innovation of large-scale construction enterprises (LSCE) under different contexts. Based on NK stochastic combinatorial optimization model with two parameters (N, K), this study proposes three modules—ambidexterity, punctuated equilibrium, and automation—to simulate the change behaviors of the inner innovation system. Furthermore, this study analyses the influence of factor correlation on inner innovation performance under discusses nine different combination situations. The research results show that (a) regardless of how the relationship strength changes, fitness increases with an increasing K value; and (b) the comparison of optimization modes show that the punctuated equilibrium mode can guarantee the long- and short-term inner innovation performance LSCE. This study therefore reveals the mutual effects of the factors in the inner innovation system in LSCE and provides an effective model for internal systems analyses in the construction industry and in other sectors

Haze Risk Assessment Based on Improved PCA-MEE and ISPO-LightGBM Model

With the economic development in China, haze risks are frequent. It is important to study the urban haze risk assessment to manage the haze disaster. The haze risk assessment indexes of 11 cities in Fenwei Plain were selected from three aspects: the sensitivity of disaster-inducing environments, haze component hazards and the vulnerability of disaster-bearing bodies, combined with regional disaster system theory. The haze hazard risk levels of 11 cities in Fenwei Plain were evaluated using the matter-element extension (MEE) model, and the indicator weights were determined by improving the principal component analysis (PCA) method using the entropy weight method, and finally, five haze hazard risk assessment models were established by improving the particle swarm optimization (IPSO) light gradient boosting machine (LightGBM) algorithm. It is used to assess the risk of affected populations, transportation damage risk, crop damage area risk, direct economic loss risk and comprehensive disaster risk before a disaster event occurs. The experimental comparison shows that the haze risk index of Xi’an city is the highest, and the full index can improve the evaluation accuracy by 4–16% compared with only the causative factor index, which indicates that the proposed PCA-MEE-ISPO-LightGBM model evaluation results are more realistic and reliable

Haze Risk Assessment Based on Improved PCA-MEE and ISPO-LightGBM Model

With the economic development in China, haze risks are frequent. It is important to study the urban haze risk assessment to manage the haze disaster. The haze risk assessment indexes of 11 cities in Fenwei Plain were selected from three aspects: the sensitivity of disaster-inducing environments, haze component hazards and the vulnerability of disaster-bearing bodies, combined with regional disaster system theory. The haze hazard risk levels of 11 cities in Fenwei Plain were evaluated using the matter-element extension (MEE) model, and the indicator weights were determined by improving the principal component analysis (PCA) method using the entropy weight method, and finally, five haze hazard risk assessment models were established by improving the particle swarm optimization (IPSO) light gradient boosting machine (LightGBM) algorithm. It is used to assess the risk of affected populations, transportation damage risk, crop damage area risk, direct economic loss risk and comprehensive disaster risk before a disaster event occurs. The experimental comparison shows that the haze risk index of Xi’an city is the highest, and the full index can improve the evaluation accuracy by 4–16% compared with only the causative factor index, which indicates that the proposed PCA-MEE-ISPO-LightGBM model evaluation results are more realistic and reliable

Analysis of Spatio-Temporal Characteristics and Trend Forecast of Building Industry VOCs Emissions in China

Emissions of volatile organic compounds (VOCs) from the building industry in China are increasing. Predicting future trends in China’s building industry VOCs will help the implementation of China’s construction VOCs emission reduction policy. The aim of this study is to combine Granger causality analysis, Ridge regression, GM(1,N), and categorical boosting (CatBoost) methods for the analysis of factors influencing and trend prediction of VOCs emissions from building industry in China. Firstly, the spatial and temporal characteristics of building industry VOCs emissions in China were calculated, based on building industry VOCs emissions data from 2006 to 2020 for each province and city in China. Secondly, Granger causality tests and STIRPAT models were used to examine the influencing factors of construction VOCs, combined with ridge regression to estimate the elasticity coefficients of the influencing factors and feature screening, which were used as input features for prediction. Finally, a combination of the improved GM(1,N) model and the CatBoost model was used to predict and compare the results with those of the GM(1,N) model, the support vector regression model (SVR), the random forest (RF), and the CatBoost model. The results show that the combined model with improved GM(1,N) and CatBoost has better prediction accuracy than the other models. China’s building industry VOCs emissions are increasing year on year and are not expected to reach their peak by 2030. The size of the population, the number of people employed in the building industry, and the area of housing floor space under construction are important influencing factors that affect VOCs emissions from building industry in China. Based on the predicted results for the different scenario settings, building industry VOCs emissions are lower in the short term for the baseline scenario and in the long term for the high-speed scenario

Prediction of Air Pollutant Concentration Based on One-Dimensional Multi-Scale CNN-LSTM Considering Spatial-Temporal Characteristics: A Case Study of Xi’an, China

Air pollution has become a serious problem threatening human health. Effective prediction models can help reduce the adverse effects of air pollutants. Accurate predictions of air pollutant concentration can provide a scientific basis for air pollution prevention and control. However, the previous air pollution-related prediction models mainly processed air quality prediction, or the prediction of a single or two air pollutants. Meanwhile, the temporal and spatial characteristics and multiple factors of pollutants were not fully considered. Herein, we establish a deep learning model for an atmospheric pollutant memory network (LSTM) by both applying the one-dimensional multi-scale convolution kernel (ODMSCNN) and a long-short-term memory network (LSTM) on the basis of temporal and spatial characteristics. The temporal and spatial characteristics combine the respective advantages of CNN and LSTM networks. First, ODMSCNN is utilized to extract the temporal and spatial characteristics of air pollutant-related data to form a feature vector, and then the feature vector is input into the LSTM network to predict the concentration of air pollutants. The data set comes from the daily concentration data and hourly concentration data of six atmospheric pollutants (PM2.5, PM10, NO2, CO, O3, SO2) and 17 types of meteorological data in Xi’an. Daily concentration data prediction, hourly concentration data prediction, group data prediction and multi-factor prediction were used to verify the effectiveness of the model. In general, the air pollutant concentration prediction model based on ODMSCNN-LSTM shows a better prediction effect compared with multi-layer perceptron (MLP), CNN, and LSTM models

Ion Flux in Roots of Chinese Fir (Cunninghamia lanceolata (Lamb.) Hook) under Aluminum Stress.

Chinese fir is a tall, fast-growing species that is unique to southern China. In Chinese fir plantations, successive plantings have led to a decline in soil fertility, and aluminum toxicity is thought to be one of the main reasons for this decline. In this study, Non-invasive Micro-test Technology was used to study the effect of aluminum stress on the absorption of 4 different ions in the roots of the Chinese fir clone FS01. The results are as follows: with increased aluminum concentration and longer periods of aluminum stress, the H+ ion flow gradually changed from influx into efflux; there was a large variation in the K+ efflux, which gradually decreased with increasing duration of aluminum stress; and 1 h of aluminum stress uniformly resulted in Ca2+ influx, but it changed from influx to efflux after a longer period of aluminum stress. Changes in the different concentrations of aluminum had the largest influence on Mg2+