Weather Pollution Research Articles

Vertical Distribution of Water Vapor During Haze Processes in Northeast China Based on Raman Lidar Measurements

Haze refers to an atmospheric phenomenon with extremely low visibility, which has significant impacts on human health and safety. Water vapor alters the scattering properties of atmospheric particulate matter, thus affecting visibility. A comprehensive analysis of the role of water vapor in haze formation is of great scientific significance for forecasting severe pollution weather events. This study investigates the distribution characteristics and variations of water vapor during haze weather in Changchun City (44°N, 125.5°E) in autumn and winter seasons, aiming to reveal the relationship between haze and atmospheric water vapor content. Analysis of observational results for a period of two months (October to November 2023) from a three-wavelength Raman lidar deployed at the site reveals that atmospheric water vapor content is mainly concentrated below 5 km, accounting for 64% to 99% of the total water vapor below 10 km. Furthermore, water vapor content in air pollution exhibits distinct stratification characteristics with altitude, especially within the height range of 1–3 km, where significant water vapor variation layers exist, showing spatial consistency with inversion layers. Statistical analysis of haze events at the site indicates a high correlation between the concentration variations of PM2.5 and PM10 and the variations in average water vapor mixing ratio (WVMR). During haze episodes, the average WVMR within 3 km altitude is 3–4 times higher than that during clear weather. Analysis of spatiotemporal height maps of aerosols and water vapor during a typical haze event suggests that the relative stability of the atmospheric boundary layer may hinder the vertical transport and diffusion of aerosols. This, in turn, could lead to a sharp increase in aerosol extinction coefficients through hygroscopic growth, thereby possibly exacerbating haze processes. These observational findings indicate that water vapor might play a significant role in haze formation, emphasizing the potential importance of observing the vertical distribution of water vapor for better simulation and prediction of haze events.

Transport Characteristics and Potential Source Areas of PM2.5 in Yangquan City Under Different Pollution Levels

Based on the PM2.5 monitoring data, NCEP data, and the meteorological data of the weather situation analysis at the corresponding time in Yangquan City from 2020 to 2022, using the HYSPLIT4 backward trajectory model, multi-station potential source contribution factor analysis （MS-PSCF） and trajectory density analysis （TDA） were introduced to study the differentiation and classification of PM2.5 transport channels and potential sources in Yangquan City. The results showed that： ① The PM2.5 pollution in Yangquan was mainly concentrated in Yangquan and Pingding, whereas the pollution in Yuxian was relatively light. The proportion of days with different pollution levels and the average and maximum values of PM2.5 concentration in Yangquan and Pingding were significantly higher than those in Yuxian, and the distribution characteristics of PM2.5 were closely related to the local special terrain. ② The amount of PM2.5 pollution and the concentration of PM2.5 in different pollution levels were the highest in light wind weather. The influence of east-west regional transport on PM2.5 pollution times and PM2.5 concentration of Yangquan and Pingding was obvious, and the contribution of east wind was significant. The influence of local pollution sources was the main factor in the moderate pollution weather in Yuxian County. ③ There were four main ground conditions for the generation and maintenance of moderate or above pollution weather： warm low pressure type （22%）, high pressure front （bottom） type （54%）, high pressure back type （14%）, and pressure equalization field （10%）. High pressure front （bottom） type was the main ground situation causing the increase in PM2.5 concentration. There were two types of upper air conditions, namely, flat westerly flow type （78%） and northwest flow type （22%）. The upper westerly flow type was the main upper air condition that caused the increase in PM2.5 concentration. ④ The results of transport channels and potential source areas of PM2.5 with different pollution levels obtained by MS-PSCF and TDA were consistent. The main transport channels of PM2.5 were the northeast, southeast, and northwest channels, whereas the northeast and southeast channels were short-distance transport routes, which were the main routes causing the increase in PM2.5 concentration. The northwest channel was consistent with the northwest dust transport channel, belonging to long-distance transmission. The main potential source areas of PM2.5 pollution were located in the central and western parts of Hebei and the southeast part of Hebei, the northeast part of Henan and its junction with the southwest part of Shandong, and the southeast part of Shanxi.

Optimal emission control plan aiming to basically eliminate PM2.5 heavy pollution in North and Northwest China in 2018

The fundamental eradication of heavy air pollution is the core objective during China's "14th Five-Year Plan" period. The purpose of this study was to basically eliminate fine particulate matter (PM2.5) heavy pollution in North and Northwest China during all PM2.5 heavy pollution process throughout 2018 through optimal emission control model based on linear programming technique. The results showed that the sensitivity coefficient of PM2.5 concentration to precursor emissions in North China increased with emissions disturbances, ranging from 0.23 to 0.93 with −80% disturbances. Compared to non-optimal control (NOC) scenario in North China, the optimal control (OC) scenario implemented in various precursors in various regions not only resulted in less precursors mitigation (with emission reduction of 4.1%, 5.9%, 16.6%, 3.5%, and 4.5% for primary PM2.5, SO2, NOx, NH3, and VOCs, respectively), but also led to greater air quality improvements (with the number of PM2.5 heavy pollution days (HPDs) reduced by 37 days, achieving the goal of basically eliminate PM2.5 heavy pollution in North China). If the polluting industries in North China were transferred to Northwest China and the synergistic control (SC scenario) measures were applied, both regions could achieve basically elimination of PM2.5 heavy pollution during the whole simulation periods. However, without implementing optimal control measures in Northwest China (FU scenario), the air quality in that region was poised to deteriorate. Specifically, the number of HPDs in Northwest China changed from 95 days under business as usual (BAU) scenario to 376 days under FU scenario and 29 days under SC scenario, and no new cities with PM2.5 heavy pollution emerged under SC scenario compared to BAU scenario. This study could provide scientific support for the battle against heavy pollution weather in China by redistributing the domestic industrial layout, rather than solely reducing local emissions.

Sensitivity analysis and precursor emission sources reduction strategies of O3 for different pollution weather types based on the GRAPES-CUACE adjoint model

Sensitivity analysis and precursor emission sources reduction strategies of O3 for different pollution weather types based on the GRAPES-CUACE adjoint model

Comparative analysis of multi-directional long-distance pollution transport effects on heavily polluted weather in the Liaoning region

In recent years, although the air quality in the Liaoning region has improved, heavy pollution weather is still caused by external pollution transportation. In order to study the influence of multi-directional long-distance pollution on the heavily polluted weather in Liaoning, this paper analyzes three types of transport channel (northerly, northwesterly, and southwesterly transport corridors) by combining ground observations, airborne observations, AQI and the concentration data of atmospheric pollutants such as PM2.5, PM10 and so on. The results show that both northerly and southwesterly winds caused widespread fog and haze in the Liaoning region. The PM2.5 concentration reached the second-highest value on record (1479 μg/m3) during the pollution period caused by northwesterly wind transport. Wind and sand transported from the northwest mainly caused dusty weather in Liaoning, with PM10 concentration reaching up to 1200 μg/m3 during the pollution period. Northeasterly and northwesterly wind transport mainly led to the accumulation of pollutants in high places to high pollution values. Convergence of winds due to southwesterly wind transport led to prolonged accumulation of pollutants in the Liaoning region, resulting in severe pollution. Long-distance pollution transport is now one of the main sources of heavy pollution in the Liaoning region.

Forecasting and alert of atmospheric bioaerosol concentration profile based on adaptive genetic algorithm back propagation neural network, atmospheric parameter and fluorescence lidar

Bioaerosols are biologically originated particles in the atmosphere, which is mainly composed of bacteria, fungi, viruses, pollen, spores, and the fragmentation and disintegration of plants and animals. Bioaerosols are easy to be spread in the lower atmosphere and cause various epidemic diseases, which is harmful to human health. The forecasting and alert of bioaerosols have important scientific significance and reality needs. In this paper, a method is proposed for estimating and predicting the concentration profile of atmospheric bioaerosols using fluorescence lidar observational data. Using the powerful nonlinear prediction ability of artificial neural networks and through repeated training, a mathematical model can be established for the relationship among atmospheric environment, meteorological parameters, and bioaerosol concentration profiles. The input parameters are temperature and humidity, aerosol extinction coefficient, backscatter coefficient, PM2.5, PM10, SO2, NO2, CO, O3, and wind speed, and outputs the concentration profile of bioaerosols. The prediction results with the measurement relative deviation of genetic algorithm back propagation (GA-BP) neural network and adaptive genetic algorithm back propagation (AGA-BP) neural network were analyzed. The results indicate that the AGA-BP neural network can effectively predict the concentration distribution of bioaerosols, and the predicted concentrations of bioaerosols are 1793 particles × m−3, 3088 particles × m−3, 5261 particles × m−3, 7410 particles × m−3 and 9133 particles × m−3 for air quality with superior, fine, mild contamination, middle level pollution and heavy pollution at an altitude of 0.315 km, respectively. We found that the predicted concentration of pollution weather is much higher than that of good weather. Furthermore, the AGA-BP neural network was used to predict the concentration profiles of atmospheric bioaerosols under different weather conditions, which provided a new research method for forecasting and alert of atmospheric bioaerosols.

Weather Pollution Prediction Based on XGBoost and ARIMA: Evidence from Shanghai

As a matter of fact, the weather pollution prediction is a pretty important issue in environment analysis. With the rapid development of machine learning techniques and bigdata analysis, it is available to realize accurate prediction based on the state-of-art models. In recent years, the weather condition is pretty worse in Shanghai. With this in mind, the general situation of air quality in Shanghai was statistically analyzed using air pollution detection data such as API in Shanghai from 2014 to 2023, and the relationship between air quality influencing factors and the air quality index API was investigated based on the construction of the machine learning model and scenarios. With the analysis and the usage of XGBoost and ARIMA, one can get the future trends and prediction. According to the analysis, the impact factors are discussed as well as the current limitations are demonstrated. At the same time, the future prospects are discussed.

Prediction and Early Warning of Air Quality based on the LSTM-ARIMA Model

At present, China's economy is transforming from a stage of high-speed growth to a stage of high-quality development. Building an ecological civilization is an important part of the realization of the Chinese dream of national rejuvenation. Air pollution will cause harm to human health, ecological environment, social and economic aspects, and its pollution level is affected by many factors, such as PM2.5, PM10, CO, temperature, wind speed, precipitation and so on. In order to implement the party's 20th spirit, strengthen the coordinated control of pollutants, basically eliminate heavy pollution weather, improve and improve the response and disposal mechanism of heavy pollution weather, a place issued an emergency plan for pollution weather, which will strengthen monitoring and early warning, energy conservation and emission reduction, and minimize the impact of pollution weather. To explore the influencing factors of PM2.5 concentration change, more accurate prediction PM2.5 concentration and AQI index, this paper for the prediction and warning of air quality, using the daily pollutants in January 2015 to April 2023, the data of analysis of PM2.5 concentration, the importance of the top three factors PM10, average temperature, CO as auxiliary variables, to construct the LSTM-ARIMA combination model. First, LSTM was used for multi-factor prediction. In order to improve the accuracy of the prediction results, the prediction error of LSTM was then linearly corrected based on the ARIMA model, the time step was adjusted to observe the previous data, and the prediction results of the model were evaluated by root mean square error (RMSE).

Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants

Abstract In the present research, a statistical analysis of all pollution incidents occurring from 2015 to 2019 in the cities of the urban agglomeration centered on Shenyang was performed. The results indicated that heavy pollution mainly occurred during the heating season, and the main pollutants were (particulate matter) PM2.5 and PM10. It was also determined that the heavy pollution that occurred during the heating season in Shenyang was of the soot type. The weather research forecast-chemistry (WRF-Chem) was used to simulate the meteorological elements and particle concentration during the two heavy pollution periods in 2019 and compared the simulation data with the monitoring data to verify the simulation performance of the model. Results demonstrated that the model had a better simulation effect on temperature and pressure than on wind speed and wind direction. By comparing the hourly particle concentration data, it was found that the simulation results for pollutants obtained with the WRF-Chem model were lower than the measured values. The simulation effect on PM2.5 was better than that on PM10, and the simulation results were basically consistent in the high- and low-value areas, and the time of peak and valley was basically synchronous. It was proven that the selected parameterization scheme properly simulated the weather situation and changes in pollutants during heavy pollution events in the Shenyang area. These results verified the application value of the WRF-Chem model during the investigation of heavy pollution events.

Performance of a One-Dimensional Model of Wave-Driven Nearshore Alongshore Tracer Transport and Decay with Applications for Dry Weather Coastal Pollution.

Dry weather pollution sources cause coastal water quality problems that are not accounted for in existing beach advisory metrics. A 1D wave-driven advection and loss model was developed for a 30 km nearshore domain spanning the United States/Mexico border region. Bathymetric nonuniformities, such as the inlet and shoal near the Tijuana River estuary mouth, were neglected. Nearshore alongshore velocities were estimated by using wave properties at an offshore location. The 1D model was evaluated using the hourly output of a 3D regional hydrodynamic model. The 1D model had high skill in reproducing the spatially averaged alongshore velocities from the 3D model. The 1D and 3D models agreed on tracer exceedance or nonexceedance above a human illness probability threshold for 87% of model time steps. 1D model tracer was well-correlated with targeted water samples tested for DNA-based human fecal indicators. This demonstrates that a simple, computationally fast, 1D nearshore wave-driven advection model can reproduce nearshore tracer evolution from a 3D model over a range of wave conditions ignoring bathymetric nonuniformities at this site and may be applicable to other locations.

Outdoor Radon and Its Progeny in Relation to the Particulate Matter during Different Polluted Weather in Beijing

This study aimed to investigate the differences in the relationship between radon and its progeny concentrations and particulate matter concentrations under varying pollution weather conditions. Outdoor radon and its progeny concentrations were measured by a radon/thoron- and radon/thoron progeny monitor (ERS-RDM-2S) during haze and dust storm weather in Beijing. Particulate matter concentrations and meteorological data were simultaneously recorded. Results showed that radon and its progeny concentrations exhibited a diurnal variation pattern, with a minimum in the late afternoon and a maximum in the early morning. The average radon concentrations were similar under both pollution weather conditions, but significantly higher than the reported average for Beijing. The equilibrium equivalent radon concentration during haze was about two times that during a dust storm. PM10 concentrations were similar in both pollution weather conditions, but PM2.5 concentrations during haze were approximately 2.6 times higher than that during dust storms. A positive correlation was observed between radon and its progeny concentrations and particulate matter concentrations, but the correlation was significantly higher during haze than during dust storms. The higher PM2.5 concentration during haze significantly increased the correlation between radon and its progeny concentrations and particulate matter concentrations. We recommended protecting against radon exposure during pollutant weather, especially haze.

AI climate tipping‐point discovery (ACTD)

AI climate tipping‐point discovery (ACTD)

PM2.5 Concentration Prediction Method Based on Temporal Attention Mechanism and CNN-LSTM

Accurately predicting PM2.5 concentration can effectively avoid the harm caused by heavy pollution weather to human health. In view of the non-linearity, time series characteristics, and the problem of large multi-step prediction errors in PM2.5 concentration data, a method combining Long Short-term Memory Network and Convolutional Neural Network with Time Pattern Attention mechanism (TPA-CNN-LSTM) is proposed. The method uses historical PM2.5 concentration data, historical meteorological data, and surrounding station data to predict the future 6-hour PM2.5 concentration of air quality monitoring stations. Firstly, CNN is used to obtain the spatial characteristics between multiple stations, secondly, LSTM is added after CNN to extract the temporal changes of non-linear data, and finally, to capture the key features of temporal information, Temporal Pattern Attention mechanism (TPA) is added. TPA can automatically adjust weights based on the input of each time step, and select the most relevant time step for prediction, thereby improving the accuracy of the model. An example analysis is conducted on the measured data of Beijing's air quality stations in 2018, and compared with other mainstream algorithms. The results show that the proposed model has higher prediction accuracy and performance.

A neural network model to predict spatiotemporal PM2.5 with FY-4A total precipitable water

Heavy pollution weather has been proven by many to be very dangerous to human health and the normal functioning of society and the economy. PM2.5 is one of the causes of the formation of heavily polluted weather. At present, PM2.5 prediction results are only single station values or average values for small areas, but in our practical application large area gridded prediction values are the most valuable. So, we propose a FFT-ConvLSTM (Fast Fourier Transform ConvLSTM Network) model. The model first uses FFT to extract/analyze the common oscillation period of all input features and sets the time sliding window's size according to the period's length. Finally, not only high accuracy gridded prediction is achieved, but also long time prediction is realized by frame by frame output. In this study, air pollutants, meteorological elements and FY-4A water vapor data in the Beijing-Tianjin-Hebei region are used as input features of the model, among which FY-4A water vapor data solve the drawback that other monitoring devices cannot obtain high spatiotemporal resolution water vapor data. It is shown that the best common periods are 200 h, 133 h and 103 h for plains, mountains and plateaus in the Beijing-Tianjin-Hebei region, respectively. The RMSE of the FFT-ConvLSTM model is reduced by 5.4 μg/m3, 1.46 μg/m3 and 0.95 μg/m3 compared with the ConvLSTM model under the three different regions. The FFT-ConvLSTM model reduced the three-region mean RMSE by 3.69 μg/m3 and 5.2 μg/m3 compared to the MLR and CNN models, respectively.

Change in Air Quality during 2014-2021 in Jinan City in China and Its Influencing Factors.

Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014-2021. The results indicate that the annual average concentrations of PM10, PM2.5, NO2, SO2, CO, and O3 and AQI values all declined year after year during 2014-2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM2.5 concentration was the highest in winter and PM2.5 concentration was the lowest in summer, while it was the opposite for O3 concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM2.5, and PM10. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.

京津冀区域一次沙尘污染天气的传输特征

京津冀区域一次沙尘污染天气的传输特征

Design a regional and multistep air quality forecast model based on deep learning and domain knowledge

Air pollution is an issue across the world. It not only directly affects the environment and human health, but also influences the regional and even global climate by changing the atmospheric radiation budget, resulting in extensive and serious adverse effects. It is of great significance to accurately predict the concentration of pollutant. In this study, the domain knowledge of Atmospheric Sciences, advanced deep learning methods and big data are skillfully combined to establish a novel integrated model TSTM, derived from its fundamental features of Time, Space, Type and Meteorology, to achieve regional and multistep air quality forecast. Firstly, Expectation Maximization and Min-Max algorithms are used for the interpolation and normalization of data. Secondly, feature selection and construction are accomplished based on domain knowledge and correlation coefficient, and then Sliding Time Window algorithm is employed to build the supervised learning task. Thirdly, the features of pollution source and meteorological condition are learned and predicted by CNN-BiLSTM-Attention model, the integrated model of convolutional neural network and Bidirectional long short-term memory network based on Sequence to Sequence framework with Attention mechanism, and then Convolutional Long Short-Term Memory Neural Network (ConvLSTM) integrates the two determinant features to obtain predicted pollutant concentration. The multiple-output strategy is also employed for the multistep prediction. Lastly, the forecast performance of TSTM for pollutant concentration, air quality and heavy pollution weather is tested systematically. Experiments are conducted in Beijing-Tianjin-Hebei Air Pollution Transmission Channel (“2+26” cities) of China for multistep prediction of hourly concentration of six conventional air pollutants. The results show that the performance of TSTM is better than other benchmark models especially for heavy pollution weather and it has good robustness and generalization ability.

Analysis of PM2.5 Transmission Characteristics in Main Cities of Jinzhong Basin in Winter

In this study, we analyzed the hourly concentration data of PM10 and PM2.5 in major cities in Jinzhong basin from 2017 to 2019. The main distribution characteristics of aerosols in Jinzhong and Taiyuan were determined, and PM2.5 hourly concentration data and HYSPLIT in Jinzhong basin in winter were discussed. The results showed that the overall level of particulate matter concentration in Taiyuan was higher than that in Jinzhong, and the monthly and seasonal variation characteristics were similar. All showed high concentrations in winter and low concentrations in summer, and the highest concentration value appeared in January. The aerosol pollution caused by the static and stable weather in Jinzhong was more common than that caused by the sand and dust weather in Taiyuan. The distribution of particulate matter showed the characteristics of more intermediate values in Jinzhong and more high and fewer low values in Taiyuan, and winter was the highest incidence season of PM2.5 pollution in Jinzhong basin. PM2.5 transmission passageways in the main cities of Jinzhong basin in winter could be divided into four categories:class 1 was transmitted along the transverse valley of Taihang Mountain, and class 2 was the southeast transmission channel. Class 1 and class 2 were the short-range transmission passageways; air masses carried more moisture, and PM2.5 transmitted along such passageways allowed moisture to be absorbed more easily, increasing levels and aggravating local pollution. Class 3 was the northwest passageway, corresponding to the most serious pollution period of PM2.5 in Jinzhong basin before the arrival of cold air, which also corresponded to the dust transmission passageway. Class 4 was the Fenwei Plain passageway, corresponding to high-concentration PM2.5 pollution. Areas with dense pollution tracks (more than 100 pollution tracks) and areas with slow air flow movement (RTA pollution track end points greater than 50) easily became potential source areas of target cities (PSCF contribution greater than 0.7). The main potential source areas of PM2.5 in winter in Jinzhong (PSCF contributing more than 0.7) were mainly distributed in Linfen, Jincheng, and other places in Shanxi province, as well as in the north of Henan province, the south of Hebei province, and central and south Shaanxi province. The distribution range of main potential source areas of PM2.5 in Taiyuan in winter was wider than that in Jinzhong, including the south of Lvliang, Yangquan, Linfen, and Yuncheng and the south of Jinzhong in Shanxi, as well as most areas in southern Shaanxi, northern Henan province, and southern Hebei province. In addition, the PSCF distribution of high-value centers above 0.9 was wider than that of Jinzhong. When pollution occurs in cities that PSCF contributed more than 0.9, special attention should be paid to the influence of mutual transmission between them and cities in Jinzhong basin. Jinzhong and Taiyuan showed different distribution characteristics corresponding to the surface wind direction when light and higher pollution occur, when the wind direction near the ground in Jinzhong was E, the frequency of light and higher pollution was 8.1%; it was the highest in all wind directions. When the wind direction near the ground in Taiyuan was SSW, the frequency of light to higher polluted weather was the highest in all wind directions (5.1%). In the case of calm wind, the frequency of light to higher pollution in Taiyuan (3.4%) was higher than that in Jinzhong (0.5%).

Heavy pollution incidents in the context of “low emission” in Beijing-Tianjin-Hebei around the Chinese New Year of 2020

<p indent="0mm">Persistent air pollution in the Beijing-Tianjin-Hebei region (BTH) has become an extremely complex challenge due to the combined effects of industrial structure, regional characteristics, weather and climate, and development. Although China’s air pollution levels have reduced significantly since the <italic>Airborne Pollution Prevention and Control Action Plan</italic> and the <italic>Blue Sky Protection Campaign</italic> were implemented, the BTH remains a sensitive and vulnerable area. Such large decrease in primary pollution was mainly attributed to the substantial reductions in economic activities and associated emissions during the 2019 novel coronavirus (COVID-19) lockdown, i.e., around the Chinese New Year of 2020. Yet two consecutive severely polluting weather processes occurred in the BTH around the Chinese New Year of 2020, which have seeded doubt among the Chinese public and policymakers regarding the current scientific understanding of the mechanisms of haze pollution. The causes of formation and maintenance of pollution processes can differ significantly. The formation and maintenance of heavy pollution weather is caused by various factors, which is a complex process. Thus, it is crucial to distinguish the contribution of emissions and meteorological conditions on polluting weather, as well as distinguish the contribution of various meteorological factors on the formation and maintenance of polluting weather, for conducting effective attribution diagnostic analysis in actually environmental and meteorological impact assessment operation systems, especially in areas that are sensitive or vulnerable to air pollution. Identifying the specific meteorological conditions formed by polluting weather and establishing a comprehensive model for discriminating atmospheric dynamics and thermodynamics can provide a scientific basis for improving numerical models for air pollution potential forecasting in the future. Therefore, in this study, we focused on two consecutive severely polluting weather processes in BTH around the 2020 Chinese New Year (January to February 2020), as an ideal and unique field experiment for the prevention and control of current severe air pollution. We explored the reasons for the formation and maintenance of continuous severely polluting weather in the context of “continuous emissions reduction” and “relatively low social activity levels” from the perspective of the abnormal structure of the high–low atmospheric circulation system. Based on comprehensive diagnostic analyses, we quantified the relative contribution of each key meteorological factor to the continuous severely polluting weather in BTH by using the standardized multiple linear regression method. The results indicated that stable maintenance of low-level coastal high-pressure systems led to higher relative humidity at ground level compared with normal years and blocking systems, which are two key meteorological factors that induced persistent polluting weather in BTH. The abnormally stable blocking situation provided a special circulation background for the occurrence and maintenance of persistent heavy air pollution in BTH. The continuous and stable easterly and southerly water vapor transportation structure provided the BTH with more moisture than normal years, and it was conducive to increased moisture absorption by aerosols, especially under blocking. The “subsidence warming” effect of the high-level blocking high-pressure system facilitated the production of a “warm cover” structure in the middle of the troposphere. The presence of the anomalous warm cover structure in the troposphere facilitated the establishment of stable and high humidity weather, which was conducive to the accumulation of pollutants and continued air pollution. Dynamic systems (blocking systems) and water vapor transportation factors directly explained 46.8% of the meteorological causes of persistent heavy air polluting weather events around the 2020 Chinese New Year in BTH.

Study on Socio-Economic Factors of Haze Pollution in the Context of Sustainable Development: Based on Asymmetric Interaction Effects Perspective

Haze pollution has been a global problem plaguing people for nearly two centuries, especially in developing countries, where haze pollution is getting worse and has become the culprit that restricts global sustainable development. As the largest developing country in the world, China’s regional characteristics of haze pollution are increasingly prominent, and the coverage is expanding, which means that there is a strong spatial interaction of haze pollution. The real world is composed of asymmetric spatial relationships, and asymmetric spatial interactions are more consistent with the reality of haze pollution. To this end, this paper identifies the asymmetric spatial interaction effects of haze pollution by using the time series econometric analysis framework and then constructs asymmetric spatial weights. In this paper, the spatial panel Durbin model and spatial effect decomposition technique are used for the empirical investigation to provide new empirical evidence to reveal the socio-economic influences of haze pollution. The study finds that haze pollution in China has a significant asymmetric spatial interaction and the degree of influence varies among cities. During the sample period, there is a significant inverted U-shaped relationship between economic development and haze pollution. However, the vast majority of cities have not passed the top inflection point of the inverted U-curve and are still in the stage where haze pollution is gradually intensifying with economic growth. High population density, high energy consumption, and unreasonable energy consumption structure aggravate haze pollution, while fixed-asset investment and science and technology expenditure help to reduce haze pollution. However, only the spatial spillover effect of the energy consumption structure is significant. Therefore, the improvement and implementation of more effective socio-economic prevention and control policies in the case of asymmetric interaction is the key to effectively dealing with heavy pollution weather, fighting the defense of the blue sky, and achieving sustainable development.