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The pulp and paper industry is a major contributor to water and air pollution globally. Pulp and paper processing is an intensive energy consuming process that produces multiple contaminants that pollute water, air, and affect ecological and human health. In Canada, the National Pollutant Release Inventory (NPRI) is used to assess the release of air pollutants into the atmosphere from industrial facilities (including pulp and paper mills) and provides a repository of annual emissions reported by individual facilities. This study compared annual air emissions of carbon monoxide, nitrogen oxides, total particulate matter (TPM), PM2.5, PM10, sulphur dioxide, and volatile organic compounds from nine different pulp and/or paper mills in Atlantic Canada from three provinces (Nova Scotia, New Brunswick, and Newfoundland and Labrador) between 2002 and 2019. Results revealed that annual releases were several orders of magnitude higher than federal reporting thresholds suggested by Environment and Climate Change Canada. Pulp mills emit higher pollutant loads than those producing paper. The highest exceedance of a reporting threshold was for particulate matter (PM2.5) at Northern Pulp in Nova Scotia. The emissions of PM2.5 were on average (over a 17-year period) about 100,000% above the reporting threshold of 0.3 tonnes per year.

From the available statistical data, cement factories co-process a range of over 100 types of waste (sorted both industrial and household) being authorized for their use as combustion components in clinker ovens. Therefore, the level of emissions is different depending on the type of fuels and waste used. The amount of industrial and municipal co-processed waste in the Romanian cement industry from 2004 to 2013 was about 1,500,000 tons, the equivalent of municipal waste generated in a year for 18 cities with over 250,000 inhabitants. The objective of this paper was to evaluate the emission level of hydrochloric acid (HCl) and hydrofluoric acid (HF) at the clinker kilns at two cement factories in Romania for different annual time intervals and to do a comparative analysis, to estimate their compliance with legislation in force. The measurements results showed average emission levels of about 0.578 mg/Nm3 for HCl and about 0.100 mg/Nm3 for HF, in the first hours of the evening, but decreased at the beginning of the third tour, at about 0.385 mg/Nm3 for HCl, respectively, to about 0.085 mg/Nm3 for HF. The evolution of HCl and HF emission levels during the last 4 years showed a variable distribution of these acids.

Ammonia stripping is a pretreatment method for piggery biogas slurry, and the effectiveness of the method is affected by many factors. Based on the results of single-factor experiments, response surface methodology is adopted to establish a quadratic polynomial mathematical model relating stripping time, pH value and gas flow rate to the average removal rate of ammonia nitrogen to explore the interactions among various influencing factors, obtain optimized combined parameters for ammonia stripping, and carry out experimental verification of the parameters. The results show that when hollow polyhedral packing is adopted under operating conditions including a stripping time of 90 min, pH value of 11, gas flow rate of 28 m3/h, gas&ndash, liquid ratio of 2000 and temperature of 30 &deg, C, the average removal rate of ammonia nitrogen in biogas slurry can reach approximately 73%. The experimental value is only 4.2% different from the predicted value, which indicates that analysis on the interaction among factors influencing ammonia stripping of biogas slurry and parameter optimization of the regression model are accurate and effective.

This paper investigates the effect of the architectural design of deep learning models in combination with a feature engineering approach considering the temporal variation in the features in the case of tropospheric ozone forecasting. Although deep neural network models have shown successful results by extracting features automatically from raw data, their performance in the domain of air quality forecasting is influenced by different feature analysis approaches and model architectures. This paper proposes a simple but effective analysis of tropospheric ozone time series data that can reveal temporal phases of the ozone evolution process and assist neural network models to reflect these temporal variations. We demonstrate that addressing the ozone evolution phases when developing the model architecture improves the performance of deep neural network models. As a result, we evaluated our approach on the CNN model and showed that not only does it improve the performance of the CNN model, but also that the CNN model in combination with our approach boosts the performance of the other deep neural network models such as LSTM. The development of the CNN, LSTM-CNN, and CNN-LSTM models using the proposed approach improved the prediction performance of the models by 3.58%, 1.68%, and 3.37%, respectively.

The influence of environmental quality on the quality of urban life and on migration decisions is an important research issue in urban economics and environmental economics. Using the 2012–2014 China Labor Dynamics Survey data (CLDS), this paper uses a conditional logit model (CLM) and Instrumental Variable (IV) estimation to examine the impact of air pollution on the migrant migration decision. We find that air pollution significantly negatively impacts the migration decisions of migrants. Specifically, if the PM2.5 level of a city increases by 10 μg/m3, the probability of migrants flowing into the city will be significantly reduced by 21.2%. It shows that migrants choose to flow into cities with better spatial quality to reduce the risk of exposure to air pollution. After controlling for the characteristics of the outflow and the reasons for the flow, the impact of air pollution on migrants’ migration decisions remains robust. Heterogeneity analysis shows that middle-aged, male, married, and highly educated migrants are more sensitive to air pollution. This paper enriches the research on air pollution and labor mobility at the micro level and provides empirical evidence for policymaking related to environmental governance and labor mobility in a low-carbon society.

The siting of enterprises is important for enterprises to formulate business objectives and business strategies, both of which are crucial to the development of enterprises in the future. Although there exists an irrefutable fact that the increasingly serious environmental problems are affecting the behaviors of enterprises, how air pollution affects the siting of enterprises has received little academic attention. Therefore, using the dataset of Chinese prefecture-level cities from 2014 to 2020, this paper employs the Spatial Durbin Model to investigate the direct and spatial spillover effects of air pollution on the site selection of enterprises. In addition, this paper also establishes a mediation effect model to explore the impact mechanism of air pollution on the site selection of enterprises. The empirical results show that air pollution exerts a negative impact on both the local and spatially related regions’ enterprises’ site selection, and the above conclusion is reinforced through a series of robustness checks. The heterogeneity analysis demonstrates that air pollution has a greater inhibitory effect on the siting of low-cleaning enterprises and small-scale enterprises for the local and adjacent regions. The mechanism analysis results indicate that air pollution inhibits the siting of enterprises by reducing the local labor endowment and market scale. Our study enriches the relevant theory of air pollution and enterprises’ location nexus, and it also provides an empirical basis for the Chinese government to formulate policies related to air governance and the siting of enterprises.

Information infrastructure construction has become an essential support for the new global technological revolution and industrial change. To examine whether information infrastructure can mitigate the level of air pollution, this paper measures the development level of information infrastructure in each region using the entropy-TOPSIS method based on the data of 31 Chinese provinces from 2013 to 2020. On this basis, it explores the impact of information infrastructure on atmospheric pollution and its mechanism using spatial measures and mediating effects. The results show that: (1) Information infrastructure can effectively improve air quality, though its spatial spillover effect is not obvious. (2) In addition to directly reducing air pollution, information infrastructure can also improve air quality by influencing industrial structure upgrading, or by influencing technological innovation first and then industrial structure upgrading. By exploring the impact of information infrastructure on air pollution and its action path, this paper expects to provide some scientific reference value for the construction of information infrastructure under the background of the new global technological revolution.

The prevalent air pollution along the Yangtze River Economic Belt (YREB) possesses a significant threat to the natural environment, which further affects nearby tourism destination development. The paper seeks to assess the impact of air pollution on tourism in this region through a 2002–2012 panel data of 31 prefecture-level cities, along with geographic information system (GIS) and cluster analyses. The results reveal that air pollution is negatively associated with the number of inbound tourists along the YREB. In general, when air pollution intensifies by 1%, the number of inbound tourists decreases by 1.171%. This impact is more evident when air pollution is more severe, in the long term, and in areas that are larger, more central, and with more tourism resources. The paper contributes to the literature by addressing common limitations in previous studies and providing a more comprehensive evaluation of air pollution’s impact on inbound tourism in the YREB. Practical implications regarding public policies and development directions based on air pollution periods, regions, and tourism resource allocations are provided.

The damage of air pollution to cultural heritage is widely known. However, the quantitative effects still need to be explored at a holistic level. Different from existing research which focuses on the “model calculation” methods, this paper uses an econometric approach to assess the overall impact of air pollution on the sustainable protection of world cultural heritage in China. Based on the data of the annual monitoring report from 2014 to 2020 released by the China World Cultural Heritage Monitoring Platform, this paper uses the thermal inversion as an instrument variable of air pollution to estimate the overall effects of air pollution on all world cultural heritage sites in China. The results indicate that almost all the air pollutants (except for CO) have significantly negative effects on heritage. The damaging effects of gaseous pollutants including SO2, NO2 and O3 is greater than that of particulate pollutants such as PM2.5 and PM10. Rainfall can exacerbate the worsening effects of gaseous pollutants, but will mitigate the negative effects of particulate pollutants; the windy weather may weaken the negative impact. In addition, environmental regulations from the local government can also alleviate the negative influence of air pollution on heritage protection. This research can provide a more comprehensive environmental prevention policy reference for the protection of world cultural heritage.

Nowadays, driven by green and low-carbon development, accelerating the innovation of joint prevention and control system of air pollution and collaborating to reduce greenhouse gases has become the focus of China’s air pollution prevention and control during the “Fourteenth Five-Year Plan” period (2021–2025). In this paper, the air quality index (AQI) data of 48 cities in three major urban agglomerations of Beijing-Tianjin-Hebei, Pearl River Delta and Yangtze River Delta, were selected as samples. Firstly, the air pollution spatial correlation weighted networks of three urban agglomerations are constructed and the overall characteristics of the networks are analyzed. Secondly, an influential nodes identification method, local-and-global-influence for weighted network (W_LGI), is proposed to identify the influential cities in relatively central positions in the networks. Then, the study area is further focused to include influential cities. This paper builds the air pollution spatial correlation weighted network within an influential city to excavate influential nodes in the city network. It is found that these influential nodes are most closely associated with the other nodes in terms of spatial pollution, and have a certain ability to transmit pollutants to the surrounding nodes. Finally, this paper puts forward policy suggestions for the prevention and control of air pollution from the perspective of the spatial linkage of air pollution. These will improve the efficiency and effectiveness of air pollution prevention and control, jointly achieve green development and help achieve the “carbon peak and carbon neutrality” goals.

In recent years, with the public paying more and more attention to the problem of air pollution, the impact of air quality on migration has gradually become a growing concern. However, in the current context of cities’ efforts to “attract talent” in China, research on the impact of air pollution on the flow or dwelling willingness of young talent is relatively limited. Based on the theory of planned behavior and from the perspective of subjective perception, this paper uses a regulated model to explore the impact mechanism of air pollution perception on young talent urban settlement intentions. Taking Hangzhou as a case, this study surveyed 987 individuals who were classified as young talent to explore the impact of air pollution perception on urban settlement intentions in China. The research shows that air pollution perception has a significant impact on young talent urban settlement intentions, and this impact is achieved through the intermediary effect of residential satisfaction. Place attachment of young talent to cities cannot significantly regulate the impact of air pollution perception on residential satisfaction, but it can significantly regulate the relationship between residential satisfaction and urban settlement intentions. That is to say, although place attachment cannot reduce the decline in residential satisfaction brought by air pollution perception, it can weaken the negative impact of air pollution perception on dwelling willingness through a decline in residential satisfaction. This paper contributes to a deeper understanding of the relationship between air quality and young talent settlement intentions.

The impact of air pollution on human health is becoming increasingly severe, and economic losses are a significant impediment to economic and social development. This paper investigates the impact of air pollutants on the respiratory system and its action mechanism by using information on inpatients with respiratory diseases from two IIIA (highest) hospitals in Wuhan from 2015 to 2019, information on air pollutants, and meteorological data, as well as relevant demographic and economic data in China. This paper describes the specific conditions of air pollutant concentrations and respiratory diseases, quantifies the degree of correlation between the two, and then provides a more comprehensive assessment of the economic losses using descriptive statistical methods, the generalized additive model (GAM), cost of illness approach (COI), and scenario analysis. According to the findings, the economic losses caused by PM2.5, PM10, SO2, NO2, and CO exposure are USD 103.17 million, USD 70.54 million, USD 98.02 million, USD 40.35 million, and USD 142.38 million, for a total of USD 454.46 billion, or approximately 0.20% of Wuhan’s GDP in 2019. If the government tightens control of major air pollutants and meets the WHO-recommended criterion values, the annual evitable economic losses would be approximately USD 69.4 million or approximately 0.03% of Wuhan’s GDP in 2019. As a result, the relevant government departments must strengthen air pollution control to mitigate the impact of air pollution on population health and the associated economic losses.

Improving air quality is an urgent task for the Beijing–Tianjin–Hebei (BTH) region in China. In 2018, utilizing 365 days’ daily concentration data of six air pollutants (including PM2.5, PM10, SO2, NO2, CO and O3) at 947 air quality grid monitoring points of 13 cities in the BTH region and controlling the meteorological factors, this paper takes the implementation of the Blue Sky Defense War (BSDW) policy as a quasi-natural experiment to examine the emission reduction effect of the policy in the BTH region by applying the difference-in-difference method. Results show that the policy leads to the significant reduction of the daily average concentration of PM2.5, PM10, SO2, O3 by −1.951 μg/m3, −3.872 μg/m3, −1.902 μg/m3, −7.882 μg/m3 and CO by −0.014 mg/m3, respectively. The results of the robustness test support the aforementioned conclusions. However, this paper finds that the concentration of NO2 increases significantly (1.865 μg/m3). In winter heating seasons, the concentration of SO2, CO and O3 decrease but PM2.5, PM10 and NO2 increase significantly. Besides, resource intensive cities, non-key environmental protection cities and cities in the north of the region have great potential for air pollutant emission reduction. Finally, policy suggestions are recommended; these include setting specific goals at the city level, incorporating more cities into the list of key environmental protection cities, refining the concrete indicators of domestic solid fuel, and encouraging and enforcing clean heating diffusion.

The impact of possible emission reduction policies designed to reduce port emissions in the Port of Kaohsiung in Taiwan was analyzed, focusing on ways to reduce air pollution from CO2, CH4, N2O, PM10, PM2.5, NOx, and SOx generated by vessels, cranes, and truck tractors in the port. This paper was set up in two stages to determine how pollution reduction policies could reach the 2030 and in 2050 goals. The results showed that emissions of CO2, CH4, N2O, PM10, PM2.5, NOx, and SOx could be reduced by 46%, 26%, 25%, 77%, 77%, 76%, and 68%, respectively, in the first stage (in 2030, INDC), and by 57%, 59%, 53%, 79%, 79%, 80%, and 69%, respectively, in the second stage (in 2050, GGGRMA), as compared to 2005 data. This paper concludes as follows: (1) for vessels, the use of liquid natural gas is the best way to reduce GHGs when navigating by water; (2) for bridge cranes, electrification is the better policy during operation; (3) in the case of truck tractors, the generation of battery-electric power is the best way to reduce emissions. These policy proposals for improving air quality can be applied to all ports in Taiwan.

In China, air pollution, especially fine particulate matter (PM2.5) pollution, has become increasingly serious with the rapid economic growth that has occurred over the past 40 years. This paper aims to introduce PM2.5 pollution as a constraint in the environmental efficiency research framework through the use of panel data covering the Chinese provinces from 2001–2018. PM2.5 environmental efficiency is measured with the slack-based measure (SBM)-Undesirable-variable returns-to-scale (VRS) model, and the results show that the average PM2.5 environmental efficiency score is 0.702, which indicates inefficiency, and is U-shaped over time. The PM2.5 environmental efficiency scores are unbalanced across the eight regions and 30 provinces of China. Additionally, the relationship between PM2.5 environmental efficiency and its influencing factors is examined with a tobit model, and the empirical findings indicate that the relationship between economic development and PM2.5 environmental efficiency is an inverted U, which is the opposite of the traditional environmental Kuznets curve (EKC). In addition, technological innovation, trade dependency, and regional development each have a significantly positive effect on PM2.5 environmental efficiency. However, environmental regulations, the industrial structure, and population density have significantly negative effects on PM2.5 environmental efficiency. Finally, this paper fails to prove that foreign direct investment (FDI) has created a PM2.5 “pollution haven” in China.

The development of renewable clean energy such as hydropower can not only ensure energy security, but also help achieve the United Nation’s Sustainable Development Goals. This paper uses the annual data of 30 provinces in China from 2000 to 2017, and constructs a dynamic spatial Durbin model and a geographically weighted regression model to empirically test the dynamic impact of hydropower on haze pollution and economic growth at the national and provincial levels. The empirical results show that the promoting effect of hydropower on economic growth in Western China is less than that in Eastern China, which further aggravates the economic development gap between the eastern and western regions. In addition, the suppression effect of hydropower on the haze pollution in the western region is greater than that in the eastern region, where the haze pollution is serious. From the national level, hydropower can promote regional economic growth and inhibit haze pollution, and the spatial spillover effects of these two effects are greater than the local effects, and the long-term impact is greater than the short-term impact. The research conclusions of this paper will help China realize the sustainable development goals of energy saving and emission reduction.

The aim of this paper was to analyze the impact of air pollution and meteorological conditions on the effectiveness of recreation in selected health resorts in Poland in 2015–2019. Four municipalities with the status of health resorts were compared in terms of exposure to harmful air pollutants such as PM10, NO2, SO2, and B(a)P in PM10. In this paper, a comprehensive statistical analysis was performed by determining the basic statistics of the measurement series. In addition, analyses of the occurrence of episodes of elevated PM10 concentrations in health resorts in Poland, as well as correlation and regression analyses, were performed. Statistical analysis showed no annual mean exceedances for the air pollutants analyzed. Average annual concentrations of harmful pollutants decreased year by year in Rabka Zdrój and also in Ciechocinek. The situation was different in Sopot and Ustroń, where the average annual pollution remained at a similar level and there was no downward trend. Studies have shown that travel to spa communities for health purposes can be problematic because air quality, while not exceeding average annual standards, is not satisfactory. To effectively address public health concerns, it is also necessary to consider meteorological conditions when analyzing air quality. A detailed analysis of the impact of meteorological conditions (average air temperature, wind speed, relative humidity, and visibility) on air quality, based on forecasts, will also help in the implementation of air protection plans and strengthen the control of harmful pollutant levels. Measures to reduce the levels of harmful pollutants will affect the effectiveness of patient treatment in spas. The article presents the correct way to conduct reliable monitoring of air quality and meteorological conditions, where it is particularly important.

This paper focuses on effects of implementing zero-emission buses in public transport fleets in urban areas in the context of electromobility assumptions. It fills the literature gap in the area of research on the impact of the energy mix of a given country on the issues raised in this article. The main purpose of this paper is to identify and analyse economic effects of implementing zero-emission buses in public transport in cities. The research area was the city of Szczecin, Poland. The research study was completed using the following research methods: literature review, document analysis (legal acts and internal documents), case study, ratio analysis, and comparative analysis of selected variants (investment variant and base variant). The conducted research study has shown that economic benefits resulting from implementing zero-emission buses in an urban transport fleet are limited by the current energy mix structure of the given country. An unfavourable energy mix may lead to increased emissions of SO2 and CO2 resulting from operation of this kind of vehicle. Therefore, achieving full effects in the field of electromobility in the given country depends on taking concurrent actions in order to diversify the power generation sources, and in particular on increasing the share of Renewable Energy Sources (RES).

The impacts of fine particulate matter (PM2.5) air pollution on health outcomes, especially those of children, have attracted worldwide attention. Based on the PM2.5 concentration data of 94 countries, including the least developed countries estimated by satellite observations in nearly 20 years, this paper investigated the impacts of PM2.5 pollution on under-five mortality rate (U5MR) and analyzed the role of public service in moderating the PM2.5-mortality relationship. Results indicated that PM2.5 pollution had significantly positive influence on U5MR globally. However, the effects of fine particulate pollution on child mortality were heterogeneous in terms of their significance and degrees in countries with different levels of development. A further test based on panel threshold model revealed that public service, measured by public education spending and sanitation service, played a positive moderating role in the PM2.5-mortality relationship. Specifically, when the ratio of public education expenditure in GDP of a country exceeded the first threshold value 3.39% and the second threshold value 5.47%, the magnitude of the impacts of PM2.5 pollution on U5MR significantly decreased accordingly. When the percentage of population with access to improved sanitation facilities in a country was over 41.3%, the health damaging effects were reduced by more than half. This paper fills the current gap of PM2.5 research in least developed countries and provides key policy recommendations.

The essence of a sports training includes not only developing the skills necessary in a chosen sport but also particular care about athlete&rsquo, s health. This issue should be taken into account especially in case of children and youth engaged in sporting activities. In the paper there are issues connected to the control of physical effort abilities in the sports training of young football players and the assessment of the reaction of the body to physical exercise in city smog conditions (the environment of the city of Krak&oacute, w) and clean air conditions (the environment of the town of Głuchołazy). This paper shows that, when assessing physical effort, one can consider not nly the results of physical tests but also the reaction of the body to a given physical load. One should remember that physical load depends not only on the methods used and the range of intensity, but also on the environmental conditions, like the quality of the air. Determining the reaction of the body to physical load (performance tests), taking into account the conditions in which the training takes place, prevents overloading and sets directions for rational sports training. The analysis of the results of the study leads to three main conclusions: (1) The planning of sports training has to consider not only the methods and means of the training but also environmental factors (air pollution), (2) Physical effort in smog conditions should be done with the use of antismog face masks, (3) The arrangement of sports training (particularly for youth) should strictly take into account the environment in which the training takes place.

By constructing a quadrilateral evolutionary game model involving the central government, local governments, polluting enterprises, and the public, this paper attempts to comprehensively analyze the development and implementation of China&rsquo, s air pollution control policies. Through the quadrilateral evolutionary game model, this paper systematically studies the evolutionary stable strategies of the four parties involved and obtains 27 equilibrium points, strategy sets, and their corresponding policy performance with the help of the four-dimensional dynamic system. The research results show that there are five equilibrium points that represent the least ideal scenarios, 14 equilibrium points that represent the less than ideal scenarios, four equilibrium points that represent the ideal scenarios, three equilibrium points that represent the more than ideal scenarios, and one equilibrium point that represents the most ideal scenarios. By analyzing the eight equilibrium points that represent the ideal, more than ideal and most ideal scenarios, especially the four stable points, this paper has obtained the conditions as well as policy implications of the four stable points in China&rsquo, s air pollution control campaign.

&ldquo, The Gray Great Wall&rdquo, formed by haze pollution is an increasingly serious issue in China, and the resulting air pollution has brought severe challenges to human health, the socio-economy and the world ecosystem. Based on the facts above, this paper uses China&rsquo, s province-level panel data from 2009 to 2016, systematically measures the heterogeneous structure of regional ecological economic (eco-economic) treatment efficiency through a Super Slacks-Based Measure (SBM) model and dynamic threshold models, and analyzes the forcing mechanism of haze pollution pressure on regional eco-economic treatment efficiency from an environmental regulation perspective. Results indicated that China&rsquo, s eco-economic treatment has been vigorously promoted, which is significantly conducive to green growth upgrading. However, the process has a large developmental scope due to regional heterogeneity. Interestingly, the forcing impact of haze pollution on regional eco-economic treatment efficiency is limited by the &ldquo, critical mass&rdquo, of environmental regulations: a weak degree of regulation will facilitate an increase in regional eco-economic treatment efficiency through the forcing effect of haze pollution pressure. Once environmental regulation reaches a critical level, a stronger degree of regulation will suppress the forcing effect of haze pollution in turn and it will decrease the regional eco-economic treatment efficiency. This paper endeavors to clarify the differences, suitability and dependency in the process of ecological transformation for Chinese local governments in different regions and provide policy references for a regional ecological transformation matching system.

Environmental pollution, rapid economic growth, and other social factors have adverse effects on public health, which have consequently increased the burden of health expenditures during the last two decades. This paper provides a comprehensive analysis of carbon dioxide (CO2) emissions and the environment index, as well as economic and non-economic factors such as Gross Domestic Product (GDP) growth, foreign direct investment, population aging, and secondary education impacts on per capita government and private health expenditures in 13 emerging economies for the time period of 1994&ndash, 2017. We employ robust econometric techniques in this endeavor of panel data analysis to account for the issues of heterogeneity and cross-sectional dependence. This study applies the Lagrange Multiplier (LM) bootstrap approach to investigate the presence of panel cointegration and empirical results underscore the existence of cointegration among variables. For the execution of long-run analysis, we incorporate the two latest estimators, i.e., continuously updated-fully modified (CUP-FM) and continuously updated- bias corrected (CUP-BC). Findings of long-run elasticities have documented that the air-pollution indicators, i.e., CO2 emissions and the environment index, have a positive and significant influence on government health expenditures, while in contrast, both factors negatively influence private health expenditures in emerging economies. We find that economic factors such as GDP growth consistently show a positive impact on both government and private health expenditures, whereas, foreign direct investment exhibits a significant negative and positive impact on government and private health expenditures respectively. Findings of non-economic factors can be used to argue that population aging increases health expenditures while secondary education lowers private health spending in emerging markets. Furthermore, empirical analysis of heterogeneous causality indicates that CO2 emissions, the environment index, GDP growth, foreign direct investment, and secondary education have a unidirectional causal relationship with government and private health expenditures. Population aging has a strong relationship of bidirectional causality with government health expenditures and unidirectional causal relationship with private health expenditures. Findings of this paper put forward key suggestions for policy makers which can be used as valuable instruments for better understanding and aiming to maximize public healthcare and environmental quality gains which are highly connected with sustainable GDP growth and developments in emerging economies.

Citizens in urban areas are exposed to multiple environmental stressors like noise, heat, and air pollution, with impact on human health. There is a great deal of evidence that connects human health, objective environmental exposure, and place of residence. However, little is known about subjective and objective multiple personal exposures while being mobile. To address this research gap, this paper presents results from a mixed-methods exploratory study with cyclists in the City of Leipzig, Germany. In the summer of 2017, cyclists (n = 66) wore a unique combination of sensors that measured particle number counts (PNC), noise, humidity, temperature, geolocation, and the subjective perception of each exposure on everyday routes for one week (n = 730). A smartphone application was developed to question participants about their perception of subjective exposure. The data were analyzed with three aims: (i) to compare the multiple exposure profiles of the cyclists, (ii) to contrast the objective data and subjective individual perception, and (iii) to examine the role of route decision-making and awareness of health impacts for healthier route choices. The results indicate distinct differences between the exposure profiles of cyclists. Over 80% of the cyclists underestimated their exposure to noise and air pollution. Except for heat, no significant associations between the objective and subjective data were found. This reveals an exposure awareness gap that needs to be considered in urban health planning and risk communication. It is argued that knowledge about health impacts and route characteristics plays a crucial role in decision-making about route choices. The paper concludes with suggestions to harness smart sensing for exposure mitigation and research in health geography.

Based on the data of A-share listed companies in Shanghai and Shenzhen Stock Exchanges from 2013 to 2017 and the air quality monitoring data released by China Environmental Monitoring Station, the paper examines the impact of haze on the availability of company debt financing by using fixed-effects model and quantile regression model. The empirical results show that: Firstly, haze has a positive impact on the demand of company debt financing, and the positive effect is marginal increment. Secondly, haze has a negative impact on the availability of company debt financing, and the negative impact is also marginal increment. Further study found that heavy polluting industry characteristics weaken the impact of haze on company debt financing availability. The paper analyzes the influence of air pollution on enterprise management from the perspective of company debt financing and explains the necessity for companies to implement an environmentally sustainable development strategy.

During China&rsquo, s air pollution campaign, whistleblowing has become an important way for the central government to discover local environmental issues. The three parties involved in whistleblowing are: the central government environmental protection departments, the local government officials, and the whistleblowers. Based on these players, this paper has constructed an Evolutionary Game Model under incomplete information and introduced the expected return as well as replicator dynamics equations of various game agents based on analysis of the game agents, assumptions, and payoff functions of the model in order to study the strategic dynamic trend and stability of the evolutionary game model. Furthermore, this paper has conducted simulation experiments on the evolution of game agents&rsquo, behaviors by combining the constraints and replicator dynamics equations. The conclusions are: the central environmental protection departments are able to effectively improve the environmental awareness of local government officials by measures such as strengthening punishment on local governments that do not pay attention to pollution issues and lowering the cost of whistleblowing, thus nurturing a good governance and virtuous circle among the central environmental protection departments, local government officials, and whistleblowers. Based on the study above, this paper has provided policy recommendations in the conclusion.

With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure &amp, prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75&ndash, 80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system&rsquo, s effectiveness.

Because traffic pollution is a global problem, the prediction of traffic emissions and the analysis of their influencing factors is the key to adopting management and control measures to reduce traffic emissions. Hence, the evaluation of the actual level of traffic emissions has gained more interest. The Computer Program to calculate Emissions from Road Transport model (COPERT) is being downloaded by 100 users per month and is being used in a large number of applications. This paper uses this model to calculate short-term vehicle emissions. The difference from the traditional research was that the input velocity parameter was not the empirical value, but through reasonable conversion of the spot velocity at one point, obtained by the roadside detector, which provided new ideas for predicting traffic emissions by the COPERT model. The hybrid Autoregressive Integrated Moving Average (ARIMA) Model was used to predict spot mean velocity, after converted it to the predicted interval velocity averaged for some period, input the conversion results and other parameters into the COPERT IV model to forecast short-term vehicle emissions. Six common emissions (CO, NOX, CO2, SO2, PM10, NMVOC) of four types of vehicles (PC, LDV, HDV, BUS) were discussed. As a result, PM10 emission estimates increased sharply during late peak hours (from 15:30 p.m.&ndash, 18:00 p.m.), HDV contributed most of NOX and SO2, accounting for 39% and 45% respectively. Based on this prediction method, the average traffic emissions on the freeway reached a minimum when interval mean velocity reduced to 40 km/h&ndash, 60 km/h. This paper establishes a bridge between the emissions and velocity of traffic flow and provides new ideas for forecasting traffic emissions. It is further inferred that the implementation of dynamic velocity guidance and vehicle differential management has a controlling effect that improves on road traffic pollution emissions.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Low-cost air pollution sensors are emerging and increasingly being deployed in densely distributed wireless networks that provide more spatial resolution than is typical in traditional monitoring of ambient air quality. However, a low-cost option to measure black carbon (BC)—a major component of particulate matter pollution associated with adverse human health risks—is missing. This paper presents a new BC sensor designed to fill this gap, the Aerosol Black Carbon Detector (ABCD), which incorporates a compact weatherproof enclosure, solar-powered rechargeable battery, and cellular communication to enable long-term, remote operation. This paper also demonstrates a data processing methodology that reduces the ABCD’s sensitivity to ambient temperature fluctuations, and therefore improves measurement performance in unconditioned operating environments (e.g., outdoors). A fleet of over 100 ABCDs was operated outdoors in collocation with a commercial BC instrument (Magee Scientific, Model AE33) housed inside a regulatory air quality monitoring station. The measurement performance of the 105 ABCDs is comparable to the AE33. The fleet-average precision and accuracy, expressed in terms of mean absolute percentage error, are 9.2 ± 0.8% (relative to the fleet average data) and 24.6 ± 0.9% (relative to the AE33 data), respectively (fleet-average ± 90% confidence interval).

Due to the increase in environmental problems and air pollution during the heating period, it is important to promote clean heating in cold regions, thereby meeting the heating demand in a green manner. In order to allocate resources more effectively and facilitate the consumption of renewable energy, this paper designs an integrated heating system that incorporates geothermal energy into the framework of an integrated energy system of electricity, heating, and gas. An analysis of the environmental and economic benefits indicates that the system reduces pollutant emissions and decreases the cost of urban heating. Using an example of central heating of residential buildings in Xi’an, the paper conducts a scenario analysis based on the gas peak-shaving ratio and the ratio of geothermal heating loads to basic heating loads. The results demonstrate that the environmental and economic benefits of the integrated heating system are higher compared to central heating using coal-fired boilers. In addition, this paper conducts a sensitivity analysis of the heat source to the price factors and the load ratios. The results show that the operating costs of the integrated heating system are most sensitive to the natural gas price and the gas peak-shaving ratio. Therefore, an optimum natural gas peak-shaving ratio can be determined.

environmental protection issues and the monitoring of pollution, especially for the largest cities in Asia, are becoming increasingly prominent factors for inclusive urban planning of public open spaces. Recently, a walkability concept was implemented in many cities, and in 2016 it became a campaign direction for development in Seoul. This paper considers conditions of implementation for the walkability concept, using examples of pedestrian walkway-making initiatives, and regeneration of existing walkways along water streams in urban case studies in Seoul, South Korea. The role of nature-based solutions was considered in relation to aesthetics, and social and environmental characteristics (e.g., air pollution, oxygenation through greenery) obtained through literature reviews for the case studies. Considering the complexity of the situation, with factors such as Air Quality Index (AQI) warning conditions, and the general positive impact of walkability on enhancing a healthy life style and social interaction and on reducing congestion, this study contributes to the discussion on walkability, and the importance of nature-based urban regeneration projects for densely populated areas in cities. The results of particular cases in this paper suggest the need for careful monitoring and consideration of various factors for urban regeneration walkable design projects.

Based on the panel data of 306 cities in China from 2002 to 2012, this paper investigates China’s road transport fuel (i.e., gasoline and diesel) demand system by using the Almost Ideal Demand System (AIDS) and the Quadratic AIDS (QUAIDS) models. The results indicate that own-price elasticities for different vehicle categories range from −1.215 to −0.459 (by AIDS) and from −1.399 to −0.369 (by QUAIDS). Then, this study estimates the air pollution emissions (CO, NOx and PM2.5) and public health damages from the road transport sector under different oil price shocks. Compared to the base year 2012, results show that a fuel price rise of 30% can avoid 1,147,270 tonnes of pollution emissions; besides, premature deaths and economic losses decrease by 16,149 cases and 13,817.953 million RMB yuan respectively; while based on the non-linear health effect model, the premature deaths and total economic losses decrease by 15,534 and 13,291.4 million RMB yuan respectively. Our study combines the fuel demand and health evaluation models and is the first attempt to address how oil price changes influence public health through the fuel demand system in China. Given its serious air pollution emission and substantial health damages, this paper provides important insights for policy makers in terms of persistent increasing in fuel consumption and the associated health and economic losses.

In the current discourse surrounding economic and societal growth, much emphasis has been placed on the role and impact of digitalization. Despite this trend, research exploring the ecological implications of the digital economy remains scarce. To fill this research gap, our study aimed to investigate the correlation between the digital economy and carbon emissions, specifically examining the moderating impact of environmental regulations. For empirical analysis, we utilize the CRITIC methodology to establish a thorough set of indicators that can evaluate the performance of China’s digital economy. According to our empirical results, the digital economy seems to exert a moderating influence on the levels of carbon dioxide (CO2) emissions, and this negative impact is more pronounced in affluent and densely populated regions of China. The effectiveness of digitalization in reducing pollution can be enhanced by the enforcement of environmental regulations. This paper elucidates the potential mechanisms via which the digital economy affects carbon dioxide emissions, and constructs a framework for the mechanisms via which the digital economy affects the environment by influencing the carbon dioxide emissions, providing a new way for enterprises and governments to participate in environmental protection and expanding the content of research related to the digital economy.

To accomplish IMO’s emission reduction targets, the Chinese government has established emission control areas and implemented strict sulfur limitation policies. Faced with a downturn in the shipping industry and the challenge of an insufficient supply of compliant fuel, Hong Kong and Shenzhen in China have implemented different low-sulfur fuel oil subsidy policies. It is particularly important to study non-cooperative games between two ports considering low-sulfur fuel oil subsidies. In this paper, first, non-cooperative game models considering low-sulfur fuel oil subsidies are constructed. Second, the mechanisms of various factors affecting port pricing, throughput and profit are analyzed. Then, a case study is conducted using AIS data of container ships in Shanghai and Ningbo-Zhoushan ports. The study reveals that in both sequential and simultaneous games, the gross tonnage of a ship has an impact on the optimal service price, throughput and profit of the port. The subsidy rate has a positive impact on the profitability of the port itself, to the detriment of competitor ports. In conclusion, a low-sulfur fuel oil subsidy policy has a significant positive impact on the step-by-step implementation of more stringent air pollution reduction policies in port waters.

This paper presents the results of sorption tests against selected gaseous pollutants SO2, CO2 and H2O on geopolymer materials obtained from high-calcium fly ash from lignite power generation. In the synthesis process, activation of geopolymer materials was carried out using KOH and NaOH. It was found that the activating agent significantly affects the porous structure of the samples. The sorption experiments conducted for the KOH-activated sample showed high SO2 adsorption efficiency, almost ten times higher than against CO2. The results demonstrate the possibility of utilizing fly ash obtained from the lignite energy processing sector for the synthesis of geopolymers with potential application of the materials as functional plastering compounds.

Faced with the dual pressure from global climate change and increasing air pollution, China has gradually clarified the adoption of market-based tools to improve the ecological environment. Based on Chinese provincial panel data from 2006 to 2020, this paper analyzes the synergistic governance effect and influence mechanism of China’s carbon emissions trading scheme (ETS) on pollution control and carbon emission reduction by using a multiperiod difference-in-differences (DID) model. The empirical results show that China’s ETS has achieved the synergistic governance effect of pollution control and carbon emission reduction. The heterogeneity analysis shows that ETS can significantly enhance the synergistic governance effect in areas with higher total factor productivity (TFP) and stronger administrative intervention. More importantly, the mechanism analysis shows that China’s ETS promotes the synergistic governance effect of pollution control and carbon emission reduction mainly through industrial structure upgrading and energy structure transformation. The effect contribution values are 22.42% and 9.40%, respectively. This study contributes to deepening the effect of the carbon emissions trading scheme and also provides a reference for the design of a unified carbon trading market in China.

In recent years, the concentration levels of various air pollutants have been constantly increasing, primarily due to the high vehicle flow. In 2020, however, severe lockdowns in Greece were imposed to reduce the spread of the COVID-19 pandemic, which led to a rapid reduction in the concentration levels of air pollutants such as PM2.5 and PM10 in the atmosphere. Initially, this paper seeks to identify the correlation between the concentration levels of PM10 and the traffic flow by acquiring data from low-cost IoT devices which were placed in Thessaloniki, Greece, from March to August 2020. The correlation and the linearity between the two parameters were further investigated by applying descriptive analytics, regression techniques, Pearson correlation, and independent T-testing. The obtained results indicate that the concentration levels of PM10 are strongly correlated to the vehicle flow. Therefore, the results hint that the decrease in the vehicle flow could result in improving the quality of environmental air. Finally, the acquired results point out that the temperature and humidity are weakly correlated with the concentration levels of PM10 present in the atmosphere.

Assessment of air quality has been traditionally conducted by ground based monitoring, and more recently by manned aircrafts and satellites. However, performing fast, comprehensive data collection near pollution sources is not always feasible due to the complexity of sites, moving sources or physical barriers. Small Unmanned Aerial Vehicles (UAVs) equipped with different sensors have been introduced for in-situ air quality monitoring, as they can offer new approaches and research opportunities in air pollution and emission monitoring, as well as for studying atmospheric trends, such as climate change, while ensuring urban and industrial air safety. The aims of this review were to: (1) compile information on the use of UAVs for air quality studies; and (2) assess their benefits and range of applications. An extensive literature review was conducted using three bibliographic databases (Scopus, Web of Knowledge, Google Scholar) and a total of 60 papers was found. This relatively small number of papers implies that the field is still in its early stages of development. We concluded that, while the potential of UAVs for air quality research has been established, several challenges still need to be addressed, including: the flight endurance, payload capacity, sensor dimensions/accuracy, and sensitivity. However, the challenges are not simply technological, in fact, policy and regulations, which differ between countries, represent the greatest challenge to facilitating the wider use of UAVs in atmospheric research.

Air quality monitoring in polluted environments is of great significance to human health. Traditional methods use various pieces of meteorological equipment, which have limited applications in complex terrains and high costs. In this paper, a novel idea is put forward to solve the problem of air pollution monitoring in urban areas. We investigate whether air quality can be assessed visually by examining the haziness of photos from a far distance. Specifically, the correlation between the air quality indexes, such as the AQI, PM2.5, and PM10, of real outdoor scenarios and the haziness level evaluation scores of the monitoring images is calculated. The results show that the objective indicators can indeed reflect the level of air pollution, and the degree of correlation is invariant to the image size. To apply this new observation to a practical system, a novel method called fastDBCP (fast dark and bright channel prior) is developed. Based on a down-sampling strategy, a ratio is calculated between the dark and bright channel prior information in scaled images and adopted as the visual index of air pollution. Our experimental results show that the proposed metric not only demonstrates advantages in terms of its correlation degree and computational speed, but also shows a high level of classification accuracy compared to that of competing metrics.

Investigating the fiscal decentralization’s effect on the carbon intensity of agricultural production may assist the United States in reaching its carbon peak and becoming carbon neutral. This paper delves into the investigation of the spatiotemporal patterns and internal relationships between fiscal decentralization, agricultural carbon intensity, and environmental regulation. The goal was achieved by using the spatial Durbin model using panel data for 49 states of the United States from 2000 to 2019. The study has found that environmental regulations play a significant role in reducing regional carbon emissions in agriculture and contribute positively to carbon emissions control. However, fiscal decentralization, which grants local governments more financial autonomy, has a positive but insignificant impact on carbon emissions, indicating that the prioritization of economic development and carbon control over environmental protection is favored by local governments. In examining the impact of environmental regulations on carbon emissions, the study reveals that fiscal decentralization does not play a substantial role in moderating this relationship. To promote low-carbon agriculture projects and ensure coordinated economic and environmental development, the study recommends optimizing the fiscal decentralization system, formulating different policies for different regions, and regulating the competencies of local governments through an effective examination system. The study concludes that it is crucial to obtain data at the city or county level to accurately understand the relationship between agricultural carbon intensity, environmental regulation, and fiscal decentralization. As a result, the central government must focus on perfecting the fiscal decentralization system, developing a differentiated agricultural carbon emission control system, controlling competition among local governments, and perfecting a political performance assessment system.

Air pollution is one of the most challenging and complex problems of our time. This research presents the prediction of air pollutant values based on using an R program with linear regression. The research sample consists of obtained values of air pollutants such as sulphur dioxide (SO2), particulate matter (PM10, PM2.5), carbon monoxide (CO), nitrite oxides (NO, NO2, and NOX), atmospheric data pressure (p), temperature (T), and relative humidity (rh). The research data were collected from the city of Belgrade air quality monitoring reports, published by the Environmental Protection Agency of the Republic of Serbia. The report data were transformed into a form suitable for processing by the R program and used to derive prediction functions based on linear regression upon pairs of air pollutants. In this paper, we describe the R program that was created to enable the correlation of air pollutants with linear regression, which results in functions that are used for the prediction of pollutant values. The correlation of pollutants is presented graphically with diagrams created within the R GUI environment. The predicted data were categorized according to air pollution standard ranges. It has been shown that the derived functions from linear regression enable predictions that are well correlated with the data obtained by automatic acquisition from air quality monitoring stations. The R program was created by using R language statements without any additional packages, and, therefore, it is suitable for multiple uses in a diversity of application domains with minor adjustments to appropriate data sets.

Law reinforcement agencies can be established to improve enterprise environmental performance, a determinant of sustainable growth, but their micropractical evaluation is unclear. This paper uses panel data (1998–2014) from the Chinese Industrial Enterprise Pollution Database and the Chinese Industrial Enterprise Database and adopts the multiperiod difference-in-differences (DID) method to investigate the impact of law reinforcement on enterprise environmental performance (measured by air pollutant emissions). Using the establishment of China’s city-level environmental courts (ECs) as an identification strategy, the study finds a strong positive effect of EC establishment on firms’ environmental performance and confirms the validity of law reinforcement. Furthermore, the effects are heterogeneous for firms with different characteristics (including scale, profit, ownership, pollution intensity and location). Notably, we find that firms improve their environmental performance by changing their production pattern and energy consumption structure. Additionally, ECs could influence firms’ environmental behaviors by enhancing judicial efficiency and generating a deterrence effect. These findings show the government could improve enterprises’ environmental performance through law reinforcement but should consider the heterogeneous effect on different firms.

This paper explored the changes of six significant pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Jilin City during the coronavirus disease 2019 (COVID-19) epidemic in 2022, and compared them with the same period of previous years to analyze the impact of anthropogenic emissions on the concentration of pollutants; The Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) model was used to evaluate the effect of meteorological factors on pollutant concentration. The results showed that except for O3, the concentrations of the other five pollutants decreased significantly, with a range of 21–47%, during the lockdown period caused by the government’s shutdown and travel restrictions. Compared with the same period in 2021, the decrease of PM2.5 was only 25% of PM10. That was because there was still a large amount of PM2.5 produced by coal-fired heating during the blockade period, which made the decrease of PM2.5 more minor. A heavy pollution event caused by adverse meteorological conditions was found during the lockdown period, indicating that only controlling artificial emissions cannot eliminate the occurrence of severe pollution events. The WRF–CMAQ results showed that the lower pollutant concentration in 2022 was not only caused by the reduction of anthropogenic emissions but also related to the influence of favorable meteorological factors (higher planetary boundary layer thickness, higher wind speed, and higher temperature).

The paper discusses the problem of detecting emission sources in a low buildings area using unmanned aerial vehicles. The problem was analyzed, and methods of solving it were presented. Various data acquisition scenarios and their impact on the feasibility of the task were analyzed. A method for detecting smoke objects over buildings using stationary video sequences acquired with a drone in hover with the camera in the nadir position is proposed. The method uses differential frame information from stabilized video sequences and the YOLOv7 classifier. A convolutional network classifier was used to detect the roofs of buildings, using a custom training set adapted to the type of data used. Such a solution, although quite effective, is not very practical for the end user, but it enables the automatic generation of a comprehensive training set for classifiers based on deep neural networks. The effectiveness of such a solution was tested for the latest version of the YOLOv7 classifier. The tests proved the effectiveness of the described method, both for single images and video sequences. In addition, the obtained classifier correctly recognizes objects for sequences that do not meet some of the initial assumptions, such as the angle of the camera capturing the image.

The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in local and global emissions. The fixed cycle of traffic lights at these intersections is one of the primary reasons for this issue. To address these challenges, applying reinforcement learning to coordinating traffic light controllers has become a highly researched topic in the field of transportation engineering. This paper focuses on the traffic signal control problem, proposing a solution using a multi-agent deep Q-learning algorithm. This study introduces a novel rewarding concept in the multi-agent environment, as the reward schemes have yet to evolve in the following years with the advancement of techniques. The goal of this study is to manage traffic networks in a more efficient manner, taking into account both sustainability and classic measures. The results of this study indicate that the proposed approach can bring about significant improvements in transportation systems. For instance, the proposed approach can reduce fuel consumption by 11% and average travel time by 13%. The results of this study demonstrate the potential of reinforcement learning in improving the coordination of traffic light controllers and reducing the negative impacts of traffic congestion in urban areas. The implementation of this proposed solution could contribute to a more sustainable and efficient transportation system in the future.

Ship exhaust gas has become an essential source of air pollution in recent years. To assess the impact of ship exhaust gas on the atmospheric environment and human health, this paper studies the problem of ship exhaust gas diffusion in the port area. According to automatic identification system (AIS) data, ship exhaust gas is estimated based on the bottom-up method, and the result of emission calculation is entered into a Gaussian puff model to calculate the superposition of the diffusion of gaseous pollutants from multiple ships. In addition, the results of a case study of the diffusion of ship exhaust gas in the western area of Shenzhen Port in China show that the distribution of the NO2 concentration in the studied area is not stable, the diffusion of exhaust gas from multiple ships mainly affects some areas near large ship berths at night, and there is a small impact on the whole study area. This lays a foundation for monitoring and treating the atmospheric environment in the port area.

The effective control of air pollution to advance human health and improve well-being has risen to the forefront of discussion in recent years. Based on China’s 2019 environmental protection tax data and China Social Survey (CSS) data, this paper studies the effects of subjective air pollution and the environmental protection tax on residents’ well-being using an econometric mediation effect model. The research conclusions are as follows: (1) Subjective air pollution can significantly reduce residents’ well-being, (2) an environmental protection tax can significantly improve residents’ well-being and it can eliminate some of the negative influence of subjective air pollution on residents’ well-being, and (3) the grouping test of residents’ income, regional distribution, urban and rural structure, age structure, gender structure, and other variables shows that the effects of subjective air pollution on residents’ well-being are heterogeneous among different populations. After further endogeneity testing with the instrumental variables method, adjusting the primary variables, and altering the research procedures, the results are still robust. Based on these findings, we should vigorously promote the development of ecological civilization and good air quality and support reforming the environmental protection tax system to enhance well-being. It is also necessary to shift from a crude development model to a green industry and business model. While emphasizing social equity and production efficiency, we should ensure the synchronous development of cities and villages. Additionally, tangible steps should be implemented to raise people’s incomes, expand young people’s work options, and enhance their satisfaction. The article focuses on the impact of subjective air pollution on residents’ well-being, adding air pollution to the factors affecting well-being. Furthermore, the article finds that the environmental protection tax has two advantages: it can govern air pollution and promote green development, and, at the same time, it can enhance social harmony and improve residents’ well-being.

Digital transformation can increase lending by commercial banks, which may have an impact on economic development and technological progress, thus affecting air pollution. However, a limited amount of literature has discussed the impact of the digital transformation of commercial banks (DTCB) on air pollution. Based on city-level data from 2010 to 2020, this study used a spatial Durbin model to explore the spatial effects of DTCB on air pollution. This study shows that DTCB significantly increases air pollution in local and surrounding cities. Heterogeneity analysis shows that DTCB increases local and surrounding city air pollution in non-innovative cities and cities with low digital economy development. However, in innovative cities and cities with high digital economy development, DTCB reduces PM2.5 emissions in local and surrounding cities. Mechanism analysis shows that DTCB has no significant impact on technological innovation, but significantly promotes economic development, thus increasing air pollution. From the perspective of DTCB, this paper deepens the research on digital finance and air pollution. Against the background of DTCB, the government should guide commercial banks to apply digital technology to increase lending for technology innovation and promote DTCB to achieve the dual goals of economic development and improvement in air quality.

World economic development, technological progress, and irrational exploitation of natural resources have intensified the negative effects of economic activity, which causes more attention to be paid to environmental pollution and the deterioration of the standard of living. Therefore, over the past few years, the concept of sustainable development has experienced a period of increased interest, accompanied by changes in the attitudes and expectations of all market participants. The article attempts to analyse the relationship between air pollution and selected parameters of the residential market in Gdansk in 2010–2020. The study considered the peculiarities of the region due to its geographical location. To identify the effects of air pollution, the GLM (generalised linear models) and GRM (generalised regression models) were used with a progressive stepwise regression method. Based on the results, it was found that the existing air pollution and climatic conditions in Gdańsk have an impact on the number of apartments sold and their prices. All models were identified for the first time for monthly data, and prices were taken from the real estate sale contracts database. These indicate the advantage and novelty of the study. In addition, this paper is the first in a series of publications examining the impact of air pollution on the real estate market in Poland’s largest agglomerations. From the main model following results were obtained: (1) A statistically significant (p ≤ 0.05) factor affecting the number of sales of premises above 80 m2 on the secondary market is PM2.5. With an increase in PM2.5 by 10 µg/m3, the number of sold apartments above 80 m2 decreases on average by slightly more than 20. (2) The interaction (p ≤ 0.05) of O3 (ozone) and PM2.5 on the number of transactions affects the secondary market of flats with an area of 60–80 m2. Simultaneous to the decrease in the concentrations of O3 and PM2.5 is an increase in the number of sales of apartments in the given size in Gdańsk. (3) Simultaneous to the decrease in PM10 and NO2 concentrations due to the increased urban traffic is an increase in the price of 1 m2 of residential premises with an area of 40 m2 on the secondary market in Gdańsk.

Environmental air pollution remains a major contributor to negative health outcomes and mortality, but the relationship between socially vulnerable populations and air pollution is not well understood. Although air pollution potentially affects everyone, the combination of underlying health, socioeconomic, and demographic factors exacerbate the impact for socially vulnerable population groups, and the United States Clean Air Act (CAA) describes an obligation to protect these populations. This paper seeks to understand how air pollution monitor placement strategies and policy may neglect social vulnerabilities and therefore potentially underestimate exposure burdens in vulnerable populations. Multivariate logistic regression models were used to assess the association between being in an ozone-monitored area or not on 15 vulnerability indicators. It was found that the odds of not being in an ozone-monitored area (not covered, outside) increased for the predictor mobile homes (OR = 4.831, 95% CI [2.500–9.338] and OR = 8.066, 95% CI [4.390–14.820] for the 10 and 20 km spatial units, respectively) and decreased for the predictor multiunit structures (OR = 0.281, 95% CI [0.281–0.548] and OR = 0.130, 95% CI [0.037, 0.457] for the 10 and 20 km spatial units, respectively) and the predictor speaks English “less than well” (OR = 0.521, 95% CI [0.292–0.931] for 10 km). These results indicate that existing pollution sensor coverage may neglect areas with concentrations of highly vulnerable populations in mobile homes, and future monitoring placement policy decisions must work to address this imbalance.