Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China

Spatial and temporal changes of air quality in Shandong Province from 2016 to 2022 and model prediction

This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo's geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.

Leave for where? The impact of air quality on migration: Evidence at the city-pair level in China

We comprehensively explore the question of "Leave for where?" by utilizing city-pair level data of China spanning from 2011 to 2017. Our investigation focuses on the impact of disparities in air quality between city pairs on migration. we find that a 1% increase in the difference air quality between inflow and outflow locations raises the number of people migrating from the outflow to the inflow location by approximately 0.07%. This finding is robust after overcoming possible endogeneity problems with average wind speed as an instrumental variable. In addition, we conducted a heterogeneity analysis in terms of intention to migrate and individual characteristics, finding that individuals who migrated for work and family are more sensitive to differences in air quality between city pairs, whereas those who moved for business are not sensitive to differences in air quality. Regarding individual characteristics, differences in air quality between city pairs have a greater impact on the migration decisions of low-educated, female, and younger migrants. Further, a mechanistic analysis by constructing cohort dummy variables reveals that poor air quality is more motivational than the desire for good air quality and the crowding-out effect of air pollution on migration is more pronounced.

The Eco-Generativity Scale (EGS): A New Resource to Protect the Environment and Promote Health

(1) Background: Environmental issues are among society's most pressing concerns as they can significantly impact the environment and human health. The Eco Generativity Scale (EGS), a 28-item four-factor scale has been introduced to promote a constructive outlook on the matter. It encompasses two types of generativity, namely ecological and social generativity, as well as environmental identity and agency/pathways. The aim of the current study was to examine the EGS's psychometric properties among 375 Italian university students. (2) Methods: To evaluate the scale's factor structure, both exploratory and confirmatory factor analyses were conducted. Internal consistency was evaluated via Cronbach's alphas and McDonald's omega. Concurrent validity was analyzed with the Positive and Negative Affect Scale (PANAS), Satisfaction with life Scale (SWLS), Meaningful Life Measure (MLM), and Flourishing Scale (FS). (3) Results: The exploratory factor analysis showed the best fit for a four-factor solution. Confirmatory factor analysis revealed that a four-factor higher-order model provided the best fit to the data with good internal consistency. Furthermore, each factor and the total score showed a good concurrent validity with the PANAS, SWLS, MLM, and FS. (4) Conclusions: The Eco-Generativity Scale (EGS) showed good psychometric properties for its use in research and intervention as a promising tool to measure eco-generativity.

Labor market distortion and air pollution: An empirical analysis based on spatial effect modeling

In China, along with the rapid development of economy, air pollution has become a hot issue of public concern, particularly in many cities. The distortion in the labor factor market can cause air pollution, but the underlying mechanism is not yet clear. To investigate this question, this article examines the effect of labor market distortion on air pollution focusing on SO₂ emissions based on data of China's 283 cities during 2003-2015. The main objectives are to examine the direct and spillover effects of labor market distortion on air pollution using panel fixed-effects models, including the spatial Durbin model and the mediated-effects model. Results show that labor market distortion directly aggravates air pollution in cities. Mechanism analysis suggests that labor market distortion incurs air pollution through mechanisms of suppressing technological progress, hindering the upgrading of industrial structure, and reducing the efficiency of energy use. Divided the cities by their locations into those in eastern, central, and western regions, we find that such unfavorable effects are more prominent in eastern and western regions of the country. These findings highlight the impetus of mitigating the distorted labor market to ameliorate air quality and promote sustainable development.

A nonseparable undesirable output modified three-stage data envelopment analysis application for evaluation of agricultural green total factor productivity in China

Agricultural sector is the basic industry providing food for human and supporting the development of national economy. The agricultural green total factor productivity (AGTFP) plays a significant role in coordinating agriculture sustainable development and pollution abatement. We employ a nonseparable undesirable output modified three-stage data envelopment analysis to evaluate the AGTFP of China's 30 provinces from 2000- 2018. We construct a more comprehensive AGTFP measurement indicator system, including seven separable inputs and three non-separable inputs and one separable good output and two non-separable bad outputs. The empirical results demonstrate that it is necessary to run a stochastic frontier analysis to eliminate the influence of random error and external environment. We get a more scientific and accurate results. The real AGTFP experiences an increase trend during the sample. From the spatial perspective, there is an obvious regional difference across the country. Efficiency decomposition indicates that the source of inefficiency is mainly from two undesirable outputs. Therefore, policy implications are put forward.

Urban and industrial environmental pollution control in China: An analysis of capital input, efficiency and influencing factors

With rapid urbanization and industrialization, environmental pollution caused by such activities has drawn much attention due to its adverse impacts on environmental quality and public health. Therefore, under the current background of China's ecological civilization construction, promoting the precise and scientific treatment of environmental pollution holds great significance. This paper proposes an improved perpetual inventory method to systematically measure the capital stock of urban and industrial pollution control. The efficiency of urban and industrial pollution control is measured by adopting the global data envelopment analysis (DEA) model. Then, the influencing factors of pollution control efficiency are empirically analyzed by using the spatial Tobit regression model. The results reveal that, first, the growth rate of the capital input scale of urban pollution control is greater than that of industrial pollution control, and the spatial distribution of capital input is unbalanced. Second, the efficiency of urban and industrial pollution control from 1991 to 2019 was generally low. The current efficiency values of urban and industrial pollution control are less than 0.2 and 0.5, respectively, indicating that urban and industrial pollution control are far from efficient. Third, the efficiency of urban and industrial pollution control is significantly positively related to the level of urbanization and industrialization, has a U-shaped relationship with the economic development level, and has heterogeneous effects on technology, energy intensity, government influence and foreign trade. On this basis, we provide constructive suggestions for optimizing the performance of pollution control.

The Impact of the Numbers of Monitoring Stations on the National and Regional Air Quality Assessment in China During 2013-18

China national air quality monitoring network has become the core data source for air quality assessment and management in China. However, during network construction, the significant change in numbers of monitoring sites with time is easily ignored, which brings uncertainty to air quality assessments. This study aims to analyze the impact of change in numbers of stations on national and regional air quality assessments in China during 2013-18. The results indicate that the change in numbers of stations has different impacts on fine particulate matter (PM_2.5) and ozone concentration assessments. The increasing number of sites makes the estimated national and regional PM_2.5 concentration slightly lower by 0.6-2.2 µg m^-3 and 1.4-6.0 µg m^-3 respectively from 2013 to 2018. The main reason is that over time, the monitoring network expands from the urban centers to the suburban areas with low population densities and pollutant emissions. For ozone, the increasing number of stations affects the long-term trends of the estimated concentration, especially the national trends, which changed from a slight upward trend to a downward trend in 2014-15. Besides, the impact of the increasing number of sites on ozone assessment exhibits a seasonal difference at the 0.05 significance level in that the added sites make the estimated concentration higher in winter and lower in summer. These results suggest that the change in numbers of monitoring sites is an important uncertainty factor in national and regional air quality assessments, that needs to be considered in long-term concentration assessment, trend analysis, and trend driving force analysis.

Embodied Carbon in China's Export Trade: A Multi Region Input-Output Analysis

With the rapid growth of China’s export trade and increasing pressure of domestic carbon emission reduction, the issue of carbon embodied in export trade has attracted increasing attention from academic circles. This paper has constructed a calculation model for embodied carbon in China’s export trade by using the multi-region input-output model and the international input-output data from the World Input-Output Database (WIOD) database in order to calculate the amount of embodied carbon. Our objective is to analyze the main source industry and specific sectors of embodied carbon in China’s export trade, and to provide a quantitative basis for emission reduction under the “carbon neutrality” strategy. The findings reveal that the embodied carbon in China’s export trade mainly comes from the secondary industry, which accounts for more than 90% of the total embodied carbon in export trade, while the proportions of embodied carbon in the primary industry and the tertiary industry are relatively low, about 1% and 5–7%, respectively. In terms of specific sectors, the crop and animal production and hunting sectors have the largest share (over 60%) of embodied carbon in the export trade of the primary industry; in the export trade of the secondary industry, the main sources of embodied carbon are the manufacturing sector and the power, gas, steam and air-conditioning supply sectors, respectively accounting for around 50% and 45% of the total embodied carbon in the export trade of the secondary industry; as for the tertiary industry, the transport and storage sectors have the largest share of embodied carbon in the export trade, which is around 70%. Based on the above research results, this paper has provided relevant policy recommendations, which are optimizing the export structure, improving the energy consumption structure and the carbon emissions trading system.

Analysis of Influencing Factors of Embodied Carbon in China’s Export Trade in the Background of “Carbon Peak” and “Carbon Neutrality”

Since China’s reform and opening up, especially after its accession to the World Trade Organization, its foreign trade has achieved fruitful results. However, at the same time, the extensive foreign trade growth model with high energy consumption and high pollution has also caused a rapid increase in carbon emissions. There is a large amount of embodied carbon emissions in the export trade. In order to achieve the strategic goals of “Carbon Peak” and “Carbon Neutrality’, and at the same time build a green trading system to achieve coordinated development of trade and the environment, it is of great significance to study embodied carbon emissions and how to decouple them with China’s foreign trade. This paper uses the Logarithmic Mean Divisia Index method to decompose the influencing factors of the embodied carbon in China’s export trade in order to study the impact of three factors: export scale, export structure, and carbon emission intensity. The results show that the change in export scale is the most important factor affecting the embodied carbon of China’s export trade, and the expansion of export scale has caused the growth of trade embodied carbon. Carbon emission intensity is the second influential factor, and the decline in carbon intensity would slow down the growth of trade embodied carbon, while changes in the export structure have the smallest impact on trade embodied carbon. The high carbonization of the overall export structure will cause growth of trade embodied carbon, but the tertiary industry has seen some improvement in the export structure, which could facilitate the decline of trade embodied carbon.

Association between Atrial Fibrillation Incidence and Temperatures, Wind Scale and Air Quality: An Exploratory Study for Shanghai and Kunming

As a common cardiovascular disease, atrial fibrillation has the characteristics of high morbidity, high disability, and high fatality rates, seriously endangering human health and sustainability. Some research has confirmed that environmental factors are related to the risk of illness and death from cardiovascular diseases (including atrial fibrillation), while there is still little comparison on the situation of the two cities in China. This research uses medical data in Shanghai and Kunming establishing, through two-step research, logistic models to compare the impacts on atrial fibrillation incidence to figure out the association between environmental factors (including air pollution, weather, temperature, and wind scales) and atrial fibrillation. Finally, this research shows that environmental impacts on atrial fibrillation prevalence have generality, regionality, and lagging characteristics. The result is significant for atrial fibrillation patients and provides a reliable medical theory basis for nursing measures. Besides, this research provides a prospective method of offering early warning for potential atrial fibrillation patients, helping to maintain human beings’ sustainable development.

A Comparative Study on Air Pollution Characteristics in Four Key Cities during 2013 in Guangxi Province, China

Based on ambient air quality data of the four key cities (Nanning, Liuzhou, Guilin, and Beihai) in Guangxi, China, along with an analysis of the main emission sources, topographic features, weather conditions, and backward trajectories, the variation of main air pollutants and pollution episodes in the four cities were studied. Results showed that air pollution was most serious in Liuzhou and Guilin, followed by Nanning and Beihai. PM2.5 was the dominant pollutant in each city, followed by O3, PM10, and NO2. Concentrations of SO2 and CO did not exceed their National Ambient Air Quality Standard Grade II limit values. In the cities, the concentrations of PM2.5, PM10, NO2, and SO2 were high during fall and winter and low during spring and summer, while the concentrations of O3 were high during fall and low during the other seasons. Concentrations of CO were low during summer and high during the other seasons in Nanning and Liuzhou, while they were high during spring and winter and low during summer and fall in Guilin and Beihai. In these cities, pollution episodes resulted mainly from stagnant accumulation and showed characteristics of regional pollution. However, pollution levels and durations for each city were different due to differences in main pollution sources, local geography, and weather conditions. The influences of air masses on the four cities were similar. They were mainly influenced by local emission sources in the spring, while during autumn, long-distance transportation from Hunan and Hubei was significant. In winter, air pollution in Nanning and Beihai was mostly affected by local emission sources, and that in Liuzhou and Guilin was mainly affected by long-distance transportation from the south and northeast of Guangxi.