Characterizing the Influences of Economic Development, Energy Consumption, Urbanization, Industrialization, and Vehicles Amount on PM2.5 Concentrations of China

Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm

With the continued rapid growth of urban areas, problems such as traffic congestion and environmental pollution have become increasingly common. Alleviating these problems involves addressing signal timing optimization and control, which are critical components of urban traffic management. In this paper, a VISSIM simulation-based traffic signal timing optimization model is proposed with the aim of addressing these urban traffic congestion issues. The proposed model uses the YOLO-X model to obtain road information from video surveillance data and predicts future traffic flow using the long short-term memory (LSTM) model. The model was optimized using the snake optimization (SO) algorithm. The effectiveness of the model was verified by applying this method through an empirical example, which shows that the model can provide an improved signal timing scheme compared to the fixed timing scheme, with a decrease of 23.34% in the current period. This study provides a feasible approach for the research of signal timing optimization processes.

Exploring the magnitude threshold of urban PM2.5 concentration: evidence from prefecture-level cities in China

As major carriers of modern economy and population, cities and towns are vortex centers of pollution migration, and the environmental effects brought about by China's unprecedented urbanization can be imagined, although the specific scale is still a mystery. This paper focuses on the nonlinear response mechanism of urban PM2.5 concentration to the urbanization population scale, considering that China's urbanization development path is dominated by large- and medium-sized cities. The panel data of PM2.5 concentration of Chinese cities observed by satellite during 1998-2016 are used to capture the nonlinear characteristics of panel threshold model (PTM). The estimation results of the double-threshold PTM including the quadratic term of urbanization population show that the U-shaped relationship between urbanization population and PM2.5 concentration is nonlinear adjusted by urban GDP per capita with the two thresholds of 6777 Yuan and 10,296 Yuan at 2010 constant price. When the urban GDP per capita exceeds 10,296 Yuan, the urbanized population at the turning point of the U-shaped curve is 12.967 million people, which only appears in a few super-large cities such as Beijing, Tianjin, Shanghai and Chongqing. The size matching of urban economy and population is an important follow-up of environmental policies.

Joint effects of air PM2.5 and socioeconomic dimensions on posted emotions of urban green space visitors in cities experiencing population urbanization: A pilot study on 50 cities of East China

People may perceive and expose negative sentiments in days with PM_2.5 pollutions, but evidence is still insufficient about the joint effects of PM_2.5 and socioeconomic factors on human sentiments. In this study, a total of 8032 facial photos of urban green space visitors were obtained from Sina Weibo in 50 cities of East China and rated for happy, sad, neutral scores and net positive emotion index (NPE; happy minus sad). Seasonal air PM_2.5 concentrations were collected from days when people exposed faces in cities that were categorized to medium, large, outsize, and mega sizes according to resident populations (RPs). In summer, people posted lower sad score (11.28 %) than in winter (13.51 %; P = 0.0357) and higher NPE (35.86 %) than in autumn (30.92 %; P = 0.0009). Multivariate linear regression on natural logarithms revealed that factors of gross domestic product per capita (parameter estimate: 0.45), RP (0.59), non-production electricity consumption (0.34), and length of road transport (0.34) together generated positive contributions to posted happy score, while the total retail trade of consumer goods (-1.25) and PM_2.5 (-0.50) were perceived as joint depressors on NPE. Overall, cities with more rich households and activated retail sales attracted more people who exposed smiles in weathers with PM_2.5 compared to cities where local economy is reliable on heavy industry. The summertime in mega cities will be recommended to enjoy a higher frequency to perceive satisfaction due to exposure to low PM_2.5.

Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China's Cities Based on Spatial Autocorrelation Analysis and MGWR Model

Understanding the characteristics of PM2.5 and its socioeconomic factors is crucial for managing air pollution. Research on the socioeconomic influences of PM2.5 has yielded several results. However, the spatial heterogeneity of the effect of various socioeconomic factors on PM2.5 at different scales has yet to be studied. This paper collated PM2.5 data for 359 cities in China from 2005 to 2020, as well as socioeconomic data: GDP per capita (GDPP), secondary industry proportion (SIP), number of industrial enterprise units above the scale (NOIE), general public budget revenue as a proportion of GDP (PBR), and population density (PD). The spatial autocorrelation and multiscale geographically weighted regression (MGWR) model was used to analyze the spatiotemporal heterogeneity of PM2.5 and explore the impact of different scales of economic factors. Results show that the overall economic level was developing well, with a spatial distribution trend of high in the east and low in the west. With a large positive spatial correlation and a highly concentrated clustering pattern, the PM2.5 concentration declined in 2020. Secondly, the OLS model's statistical results were skewed and unable to shed light on the association between economic factors and PM2.5. Predictions from the GWR and MGWR models may be more precise than those from the OLS model. The scales of the effect were produced by the MGWR model's variable bandwidth and regression coefficient. In particular, the MGWR model's regression coefficient and variable bandwidth allowed it to account for the scale influence of economic factors; it had the highest adjusted R2 values, smallest AICc values, and residual sums of squares. Lastly, the PBR had a clear negative impact on PM2.5, whereas the negative impact of GDPP was weak and positively correlated in some western regions, such as Gansu and Qinghai provinces. The SIP, NOIE, and PD were positively correlated with PM2.5 in most regions. Our findings can serve as a theoretical foundation for researching the associations between PM2.5 and socioeconomic variables, and for encouraging the coequal growth of the economy and the environment.

Spatiotemporal Variation in Ground Level Ozone and Its Driving Factors: A Comparative Study of Coastal and Inland Cities in Eastern China

Variations in marine and terrestrial geographical environments can cause considerable differences in meteorological conditions, economic features, and population density (PD) levels between coastal and inland cities, which in turn can affect the urban air quality. In this study, a five-year (2016-2020) dataset encompassing air monitoring (from the China National Environmental Monitoring Centre), socioeconomic statistical (from the Shandong Province Bureau of Statistics) and meteorological data (from the U.S. National Centers for Environmental Information, National Oceanic and Atmospheric Administration) was employed to investigate the spatiotemporal distribution characteristics and underlying drivers of urban ozone (O₃) in Shandong Province, a region with both land and sea environments in eastern China. The main research methods included the multiscale geographically weighted regression (MGWR) model and wavelet analysis. From 2016 to 2019, the O₃ concentration increased year by year in most cities, but in 2020, the O₃ concentration in all cities decreased. O₃ concentration exhibited obvious regional differences, with higher levels in inland areas and lower levels in eastern coastal areas. The MGWR analysis results indicated the relationship between PD, urbanization rate (UR), and O₃ was greater in coastal cities than that in the inland cities. Furthermore, the wavelet coherence (WTC) analysis results indicated that the daily maximum temperature was the most important factor influencing the O₃ concentration. Compared with NO, NO₂, and NO_x (NO_x ≡ NO + NO₂), the ratio of NO₂/NO was more coherent with O₃. In addition, the temperature, the wind speed, nitrogen oxides, and fine particulate matter (PM_2.5) exerted a greater impact on O₃ in coastal cities than that in inland cities. In summary, the effects of the various abovementioned factors on O₃ differed between coastal cities and inland cities. The present study could provide a scientific basis for targeted O₃ pollution control in coastal and inland cities.

Spatiotemporal Patterns and Dominant Factors of Urban Particulate Matter Islands: New Evidence from 240 Cities in China

With rapid urbanization and industrialization, PM2.5 pollution exerts a significant negative impact on the urban eco-environment and on residents’ health. Previous studies have demonstrated that cities in China are characterized by urban particulate matter island (UPI) phenomena, i.e., higher PM2.5 concentrations are observed in urban areas than in rural settings. How, though, nature and socioeconomic environments interact to influence UPI intensities is a question that still awaits a general explanation. To fill this knowledge gap, this study investigates spatiotemporal variations in UPI effects with respect to different climatic settings and city sizes in 240 cities in China from 2000 to 2015 using remotely sensed data and explores the effective mechanism of human–environmental factors on UPI dynamics based upon the Geographically Weighted Regression (GWR) model. In particular, a conceptual framework that considers natural environments, technology, population, and economics is proposed to explore the factors influencing UPIs. The results show (1) that about 70% of the cities in China selected exhibited UPI effects from 2000 to 2015. In addition, UPI intensities and the number of UPI-related cities decreased over time. It is noteworthy that PM2.5 pollution shifted from urban to rural areas. (2) Elevation was the most efficient driving factor of UPI variations, followed by precipitation, population density, NDVI, per capita GDP, and PM2.5 emission per unit GDP. (3) Climatic backgrounds and city sizes influenced the compositions and performance of dominant factors regarding UPI phenomena. This study provides valuable a reference for PM2.5 pollution mitigation in cities experiencing global climate change and rapid urbanization and thus can help sustainable urban developments.

Effect of Battery-Electric and Plug-In Hybrid Electric Vehicles on PM2.5 Emissions in 29 European Countries

The contribution of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) to mitigating/reducing fine particulate matter (PM2.5) emissions was researched through a panel of 29 European countries from 2010 to 2019, using the econometric technique of method of moments quantile regression (MM-QR). This research is innovative by connecting the increasing use of electric vehicles with PM2.5 emissions and using the MM-QR to explore this relationship. Two models were estimated to analyse their contribution to reducing PM2.5 in European countries. The nonlinearity of the models were confirmed. The statistical significance of the variables is strong for the upper quantiles (75th and 90th), resulting from the effectiveness of European policies to improve the environment. Electric vehicles (BEVs and PHEVs), economic growth, and urbanisation reduce the PM2.5 problem, but energy intensity and fossil fuel consumption aggravate it. This research sheds light on how policymakers and governments can design proposals to encourage electric vehicle use in European countries. To achieve the long-term climate neutral strategy by 2050, it is imperative to implement effective policies to reduce the consumption of fossil fuels and promote the adoption of electric vehicles using renewable energy sources.

The impacts of urban structure on PM2.5 pollution depend on city size and location

Many cities across the world face the challenge of severe fine particulate matter (PM_2.5) pollution. Among the many factors that affect PM_2.5 pollution, there is an increasing interest in the impacts of urban structure. However, quantifying these impacts in China has been difficult due to differences of study area and scale in existing research, as well as limited sample sizes. Here, we conducted a continental study focusing on 301 prefectural cities in mainland China to investigate the effects of urban structure, including urban size and urban compactness, on PM_2.5 concentrations. Based on PM_2.5 raster and land cover data, we used quantile regression and a general multilinear model to estimate the effects and relative contributions of urban size and urban compactness on urban PM_2.5 pollution, with explicit consideration for pollution level, urban size and geographical location. We found: (1) nationwide, the larger and more compact that cities were, the heavier the PM_2.5 pollution tended to be. Additionally, this relationship became stronger with increasing levels of pollution. (2) In general, urban size played a more important role than urban form, and there were no significant interactive effects between the two metrics on urban PM_2.5 concentrations at the national scale. (3) The impacts of urban size and form varied by city size and geographical location. The impacts of urban size were only significant for small or medium-large cities but not for large cities. Among large cities, only urban form had a significantly positive effect on urban PM_2.5 concentrations. The further north and west that cities were, the more dependent PM_2.5 pollution was on urban form, whereas the further south and east that cities were, the greater the impact of urban size. These results provide insights into how urban design and planning can be used to alleviate air pollution.

Exploring the Outdoor Recreational Behavior and New Environmental Paradigm among Urban Forest Visitors in Korea, Taiwan and Indonesia

This is international comparative research on the perception of local residents toward the natural environment in South Korea, Taiwan, and Indonesia. Through the New Ecological Paradigm (NEP) investigation, perceptions of natural environmental conservation and utilization of 664 urban forest visitors were analyzed, and the relationship between recreational behavior, NEP scores, and demographic characteristics was investigated. The three countries, with different histories, cultures, and economic development, showed statistically significant differences in all items. In terms of the NEP response score, Taiwan showed the most positive results with an average of 4.08. Frequent visits by the elderly and family were common significant factors of high NEP score for all survey locations. In the confirmatory factor analysis of latent variables for NEP, ‘limits to growth’ were significant in South Korea while ‘ecological crisis’ was more significant in Taiwan and Indonesia. Forest experience frequency was a common factor affecting NEP, indicating that frequent forest visits during leisure time are a major factor in improving the ecological paradigm.

The Direct and Spillover Effect of Multi-Dimensional Urbanization on PM2.5 Concentrations: A Case Study from the Chengdu-Chongqing Urban Agglomeration in China

The Chengdu-Chongqing urban agglomeration (CUA) faces considerable air quality concerns, although the situation has improved in the past 15 years. The driving effects of population, land and economic urbanization on PM_2.5 concentrations in the CUA have largely been overlooked in previous studies. The contributions of natural and socio-economic factors to PM_2.5 concentrations have been ignored and the spillover effects of multi-dimensional urbanization on PM_2.5 concentrations have been underestimated. This study explores the spatial dependence and trend evolution of PM_2.5 concentrations in the CUA at the grid and county level, analyzing the direct and spillover effects of multi-dimensional urbanization on PM_2.5 concentrations. The results show that the mean PM_2.5 concentrations in CUA dropped to 48.05 μg/m³ at an average annual rate of 4.6% from 2000 to 2015; however, in 2015, there were still 91% of areas exposed to pollution risk (>35 μg/m³). The PM_2.5 concentrations in 92.98% of the area have slowly decreased but are rising in some areas, such as Shimian County, Xuyong County and Gulin County. The PM_2.5 concentrations in this region presented a spatial dependence pattern of "cold spots in the east and hot spots in the west". Urbanization was not the only factor contributing to PM_2.5 concentrations. Commercial trade, building development and atmospheric pressure were found to have significant contributions. The spillover effect of multi-dimensional urbanization was found to be generally stronger than the direct effects and the positive impact of land urbanization on PM_2.5 concentrations was stronger than population and economic urbanization. The findings provide support for urban agglomerations such as CUA that are still being cultivated to carry out cross-city joint control strategies of PM_2.5 concentrations, also proving that PM_2.5 pollution control should not only focus on urban socio-economic development strategies but should be an integration of work optimization in various areas such as population agglomeration, land expansion, economic construction, natural adaptation and socio-economic adjustment.

Do economic development and population agglomeration inevitably aggravate haze pollution in China? New evidence from spatial econometric analysis

With sustained economic development, China's ecological environment is becoming increasingly fragile and the problem of haze pollution is becoming increasingly prominent, which has affected the normal life of human beings and the stable development of society. In this paper, 287 cities' panel data from 1998 to 2016 are used, PM_2.5 is used to represent haze pollution, and the spatial Durbin model is used to explore the role of the economy and population agglomeration on smog pollution. The empirical results show that (1) haze pollution has obvious spatial spillover. From the perspective of China as a whole, the relationship between the economy and smog pollution is an inverted U shape. (2) China is divided into three economic regions, i.e., the east, the middle, and the west. In the east and middle regions, it is found that economic development also shows an inverted U-shaped relationship with haze pollution. (3) Regardless of the country or the three major economic regions, population agglomeration is the primary factor that aggravates haze pollution; the progress of technology and the optimization of the industrial structure can improve haze pollution. (4) Through further analysis of the indirect effects of haze in China, it is found that there is a significant spatial spillover effect. According to the results of this research, policy suggestions are put forward.

Research on Environmental Sustainability of Coal Cities: A Case Study of Yulin, China

Coal cities are an essential impetus for economic development and urbanization processes in China. However, a series of environmental issues provoked by resource exploitation cause the environmental sustainability of coal cities to face enormous challenges. Therefore, on the basis of the time series data of Yulin City from 1996 to 2017, this paper explores the nexus between socioeconomic development and industrial “three wastes” emissions by adopting the Tapio decoupling model, the environmental Kuznets curve (EKC) hypothesis, and the vector auto-regressive (VAR) model. The results show that Yulin’s economic development remains in an extensive stage and will not decouple from the environmental pollution in a short time. Except for the nexus of industrial solid waste and economic growth, which is an inverted U-shaped, the EKC hypothesis is not valid for industrial wastewater and industrial waste gas. Through the VAR (2) model, the impact of per capita gross domestic product (GDP) on industrial waste emissions is consistent with the results of the EKC hypothesis. Moreover, industrial waste emissions have a positive correlation with the per capita raw coal output, the energy consumption per unit of GDP, and the proportion of secondary industry. Hence, it is necessary to formulate targeted measures from industrial restructuring, industrial chain extension, governance model optimization, and waste comprehensive utilization to realize the environmental sustainability of coal cities.

Identification of Industrial Land Parcels and Its Implications for Environmental Risk Management in the Beijing–Tianjin–Hebei Urban Agglomeration

Due to rapid, sprawling urban and industrial development, urbanization in China has led to serious environmental pollution with subsequent risks to human well-being. Landscapes comprised of intermingled residential and industrial areas are common across China, which is a large challenge for effective urban planning and environmental protection. Being able to identify industrial land across the urban landscape is critical for understanding patterns of urban design and subsequent consequences for the environment. Here, we describe a method to quickly identify industrial parcels using points of interest (POIs) and large-scale spatial data. We used the Beijing–Tianjin–Hebei urban agglomeration as a case study and identified 8325 square kilometers of industrial land, accounting for 30.7% of the total built land. Based on ground-truth randomly-sampled sites, the accuracy, precision, and recall of identified industrial areas were 87.1%, 66.4%, and 68.1%, respectively. Furthermore, we found that over 350 km2 of the industrial parcels were high human settlement risks and mainly were distributed in Tianjin and Tangshan city. Over 28.8% of the identified industrial land parcels might be at the risk of potential soil contamination. The results can be helpful in future urban planning and for identifying urban areas that are targets for implementing environmental risk management and remediation.

Socioeconomic factors and regional differences of PM2.5 health risks in China

China is a country with one of the highest concentrations of airborne particulate matter smaller than 2.5 μm (PM_2.5) in the world, and it has obvious spatial-distribution characteristics. Areas of concentrated population tend to be regions with higher PM_2.5 concentrations, which further aggravate the impact of PM_2.5 pollution on population health. Using PM_2.5-concentration and socioeconomic data for 225 cities in China in 2015, we adopted a PM_2.5-health-risk-assessment method (with simplified calculation) and applied the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to analyze the effects of socioeconomic factors on PM_2.5 health risks. The results showed that: (1) At the national level, the order of contribution degree of each socioeconomic factor in the PM_2.5-health-risk and PM_2.5-concentration model is consistent. (2) From a regional perspective, in all three regions, the industrial structure is the decisive factor affecting PM_2.5 health risks, and reduction of energy intensity increases PM_2.5 health risks, but the impact of the total amount of urban central heating on PM_2.5 health risks is very low. In the eastern region, the increased urbanization rate and length of highways significantly increase PM2.5 health risks, but the increasing effect of the extent of built-up area is the lowest. In the central region, the increasing effects of the extent of built-up area on PM_2.5 health risks are significantly greater than the decreasing effects of the urbanization rate. In the western region, economic development has the least effect on reducing PM_2.5 health risks. Our research enriches PM_2.5-health-risk theory and provides some theoretical support for PM_2.5-health-risk diversity management in China.

PM2.5 Pollution and Inhibitory Effects on Industry Development: A Bidirectional Correlation Effect Mechanism

In this paper, a vector autoregression (VAR) model has been constructed in order to analyze a two-way mechanism between PM_2.5 pollution and industry development in Beijing via the combination of an impulse response function and variance decomposition. According to the results, long-term equilibrium interconnection was found between PM_2.5 pollution and the development of primary, secondary, and tertiary industries. One-way Granger causalities were found in the three types of industries shown to contribute to PM_2.5 pollution, though the three industries showed different scales of influences on the PM_2.5 pollution that varied for about 1–2 years. The development of the primary and secondary industries increased the emission of PM_2.5, but the tertiary industry had an inhibitory effect. In addition, PM_2.5 pollution had a certain inhibitory effect on the development of the primary and secondary industries, but the inhibition of the tertiary industry was not significant. Therefore, the development of the tertiary industry can contribute the most to the reduction of PM_2.5 pollution. Based on these findings, policy-making recommendations can be proposed regarding upcoming pollution prevention strategies.

Trans-Provincial Convergence of Per Capita Energy Consumption in Urban China, 1990–2015

Recognizing the change in regulation of energy consumption may help China to control total energy consumption and realize sustainable development during rapid urbanization and industrialization. This paper re-examined the trans-provincial convergence of per capita energy consumption from 1990–2015 using five different kinds of methods for 30 Chinese provinces. Results show that per capita energy consumption across Chinese provinces was convergent. However, the results obtained by different methods were slightly different. First, it shows a weak beta-unconditional convergence during the entire period, as well as a significant beta-unconditional and conditional piecewise convergence from 1990–2000 and 2001–2015. Second, it shows a significant sigma-convergence indicated by a marked decrease in the standard deviation of logarithm (SDlog) and the coefficient of variation (CV). Third, the kernel density curve became narrower during 1990–2015, indicating that the per capita energy consumption of each Chinese province converged to a common equilibrium level, which was about 80% of the national average. Fourth, the intra-distributional mobility index implied a weak gamma-convergence. Fifth, the first difference of DF (Dickey-Fuller), ADF (Augmented Dickey-Fuller), and PP (Phillips-Perron) unit-root tests all suggested a stochastic convergence. On the whole, the results from this paper contribute to a more in-depth understanding of the status quo of per capita energy consumption in China, as well as a meaningful implication for differentiated energy policies and sustainable development strategies.