Share Of Secondary Industry Research Articles

Fostering sustainability: unveiling the impact of Internet development on carbon emissions in China.

The rapid development of the Internet has significantly impacted various socio-economic activities. Using Chinese Industrial Enterprise database and Industrial Enterprise Pollution database, this research examines the impact and mechanisms of Internet development on CO2 emissions. The key findings are as follows: (1) Internet development has substantially reduced the CO2 intensity of enterprises, and this conclusion remains robust even after performing a series of robustness analyses. (2) The major mechanisms responsible for the reduction in CO2 emissions are productivity improvement, technological innovation, and energy structure adjustment. (3) The analysis of heterogeneity reveals that the effect of Internet development on CO2 reduction is more pronounced in coastal areas, areas with a high share of secondary industry, low-carbon industries, clean industries, small-scale enterprises, and export enterprises. This study provides empirical evidence supporting China's "Internet+" strategy and its progress towards achieving the "Carbon Peaking and Carbon Neutrality Goals."

Spatial-temporal evolution patterns and drivers of PM2.5 chemical fraction concentrations in China over the past 20 years.

The quantitative assessment of the spatial and temporal variability and drivers of fine particulate matter (PM2.5) fraction concentrations are important for pollution control and public health preservation in China. In this study, we investigated the spatial temporal variation of PM2.5 chemical component based on the PM2.5 chemical component datasets from 2000 to 2019 and revealed the driving forces of the differences in the spatial distribution using geodetector model (GD), multi-scale geographically weighted regression model (MGWR), and a two-step clustering approach. The results show that: the PM2.5 chemical fraction concentrations show a trend of first increasing (2000-2007) and then decreasing (2007-2019). From 2000 to 2019, the change rates of PM2.5, organic matter (OM), black carbon (BC), sulfates (SO2- 4), ammonium (NH+ 4), and nitrates (NO- 3) were -0.59, -0.23, -0.07, -0.15, -0.02, and 0.04μg/m3/yr in the entirety of China. The secondary aerosol (i.e., SO2- 4, NO- 3, and NH+ 4; SNA) had the highest fraction in PM2.5 concentrations (55.6-68.1% in different provinces), followed by OM and BC. Spatially, North, Central, and East China are the regions with the highest PM2.5 chemical component concentrations in China; meanwhile, they are also the regions with the most significant decrease in PM2.5 chemical fraction concentrations. The GD and MGWR model shows that among all variables, the number of enterprises, disposable income, private car ownership, and the share of secondary industry non-linearly enhance the differences in the spatial distribution of PM2.5 component concentrations. Electricity consumption has the strongest influence on NH+ 4 emissions in Northwest China and BC and OM emissions in Northeast China.

An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak

With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM2.5, the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation. In this study, we construct a modern deep learning model based on multi-source data, which includes remotely sensed AOD data products, other reanalysis element data, and ground monitoring station data. Combining satellite remote sensing techniques, we finally realize a high spital density PM2.5 concentration change investigation method, and analyze the seasonal and annual, the spatial and temporal characteristics of PM2.5 concentrations in Mid-Eastern China from 2016 to 2021 and the impact of epidemic closure and control measures on regional and provincial PM2.5 concentrations. We find that PM2.5 concentrations in Mid-Eastern China during these years is mainly characterized by “north-south superiority and central inferiority”, seasonal differences are evident, with the highest in winter, the second highest in autumn and the lowest in summer, and a gradual decrease in overall concentration during the year. According to our experimental results, the annual average PM2.5 concentration decreases by 3.07 % in 2020, and decreases by 24.53 % during the shutdown period, which is probably caused by China's epidemic control measures. At the same time, some provinces with a large share of secondary industry see PM2.5 concentrations drop by more than 30 %. By 2021, PM2.5 concentrations rebound slightly, rising by 10 % in most provinces.

Surface ozone pollution in China: Trends, exposure risks, and drivers.

Within the context of the yearly improvement of particulate matter (PM) pollution in Chinese cities, Surface ozone (O3) concentrations are increasing instead of decreasing and are becoming the second most important air pollutant after PM. Long-term exposure to high concentrations of O3 can have adverse effects on human health. In-depth investigation of the spatiotemporal patterns, exposure risks, and drivers of O3 is relevant for assessing the future health burden of O3 pollution and implementing air pollution control policies in China. Based on high-resolution O3 concentration reanalysis data, we investigated the spatial and temporal patterns, population exposure risks, and dominant drivers of O3 pollution in China from 2013 to 2018 utilizing trend analysis methods, spatial clustering models, exposure-response functions, and multi-scale geographically weighted regression models (MGWR). The results show that the annual average O3 concentration in China increased significantly at a rate of 1.84 μg/m3/year from 2013 to 2018 (160 μg/m3) in China increased from 1.2% in 2013 to 28.9% in 2018, and over 20,000 people suffered premature death from respiratory diseases attributed to O3 exposure each year. Thus, the sustained increase in O3 concentrations in China is an important factor contributing to the increasing threat to human health. Furthermore, the results of spatial regression models indicate that population, the share of secondary industry in GDP, NOx emissions, temperature, average wind speed, and relative humidity are important determinants of O3 concentration variation and significant spatial differences are observed. The spatial differences of drivers result in the spatial heterogeneity of O3 concentration and exposure risks in China. Therefore, the O3 control policies adapted to various regions should be formulated in the future O3 regulation process in China.

The economic impacts of COVID-19 and city lockdown: Early evidence from China

As the first major developing country heavily struck by the COVID-19 pandemic, China adopted the world's most stringent lockdown interventions to contain the virus spread. Using macro- and micro-level data, this paper shows that both the pandemic and lockdown policies have had negative and significant impacts on the economy. Gross regional product (GRP) fell by 9.5 and 0.3 percentage points in cities with and without lockdown interventions, respectively. These impacts represent a dramatic recession from China's average growth of 6.74% before the pandemic. The results indicate that lockdown explains 2.8 percentage points of the GDP loss. We also document significant spill-over effects of the pandemic in adjacent areas but no such effects of lockdown. Reduced labor mobility, land supply, and entrepreneurship are among the most significant mechanisms underpinning the impacts of the pandemic and lockdown. Cities with higher share of secondary industry, higher traffic intensity, lower population density, lower internet access, and lower fiscal capacity suffered more. However, these cities seem to have recovered well from the recession and quickly closed the economic gap in the aftermath of the pandemic and city lockdown. Our findings have broader implications for the global interventions in pandemic containment.

How does foreign direct investment improve urban air quality?

Foreign direct investment and environmental pollution have been a prominent topic in China's economic development. Therefore, this paper investigates the relationship between foreign direct investment and environmental pollution, and makes practical policy recommendations to support the harmonious development of economy and environment based on the study findings. In this paper, the spatial econometric approach framework with the spatial weight matrix of the Delaunay method is used to examine the influence of foreign direct investment on the level of urban air quality. It uses air quality index as an indicator to measure air pollution comprehensively. Based on the spatial autocorrelation with the aggregation and radiation effects of foreign direct investment, the results show that the distribution of air pollution has considerable spatial impact in terms of spatial nonequilibrium. Through the empirical analysis, our finding reveals that the increase in GDP, the share of secondary industry, and population growth are the important elements affecting the change in air quality.

Digital development, environmental regulation, and electric power utilization efficiency

To further promote green and sustainable development, the Chinese government has put forward the carbon peaking and carbon neutrality goals in 2020. As a clean energy, electric power can effectively replace the use of traditional fossil energy and ultimately reduce environmental pollution. Under the relevant background, in order to further explore the impact mechanism of digital development on China’s electric power utilization efficiency, a static panel regression model of provincial panel data was established on the basis of measuring the electric power utilization efficiency of 30 provinces in China from 2011 to 2020. The impact of digital development on electric power utilization efficiency of China and the moderating effect of environmental regulation are analyzed. The empirical evidence yields the following conclusions: 1) Digital development has a significant positive impact on the electric power utilization efficiency in China. 2) The results of moderating effect analysis and heterogeneity analysis show that strengthening environmental regulation can promote the improvement of electric power utilization efficiency; Digital development has different impacts on electric power utilization efficiency in the east, central and west of China, especially in the west. 3) In addition, the increase of the Consumer price index of hydropower and fuel and Share of R&D expansion in regional GDP will promote the improvement of electric power utilization efficiency, while the Share of secondary industry in regional GDP will have a negative impact on it.

Is environmental tax legislation effective for pollution abatement in emerging economies? Evidence from China

This study estimates the effect of environmental tax legislation on air pollution, using the implementation of China’s Environmental Protection Tax Law (EPTL) as a quasi-natural experiment. For cities which have been authorized to raise tax rates by the EPTL, the air quality index (AQI) is 2.36 lower after the reform. The effect is reinforced in cities with stricter tax enforcement, lower fiscal stress, as well as higher initial pollution levels. Heterogeneity analyses show that the reform is more effective in cities with lower levels of marketization and legalization, as well as in developed cities. In addition, the impact of the reform is more significant in cities with higher levels of public participation in environmental governance, higher tax competition levels, and higher share of secondary industry. A series of robustness tests corroborates the results. This paper provides evidence that environmental tax legislation is efficacious in pollution abatement for developing economies.

CO2 emissions are first aggravated and then alleviated with economic growth in China: a new multidimensional EKC analysis.

CO2 emissions have become a topical issue worldwide, but few studies have considered the spatial effect of income on carbon emissions and explored the relationship between CO2 emissions and income by establishing direct, indirect, and total environmental Kuznets curves (EKCs). Using an annual panel dataset collected over the 1997-2017 period in China, this study first analyzed the spatiotemporal evolutionary process of CO2 emissions and subsequently developed direct, indirect, and total EKC-based spatial Durbin model (SDM) and partial derivative approach. These results indicate that, first, CO2 emissions have characteristic positive spatial autocorrelation, with gravity centers that have shifted westward. Second, the direct EKC forms a line, while the total EKC resembles a lying-S shape as well as the total EKC, which indicates that compared to local economic growth, neighboring growth plays a very different role in impacting local CO2 emissions. Furthermore, neighboring economic growth seems to have stronger impacts on local emissions, and the turning point of the total EKC comes much earlier than that of the conventional EKC due to the spillover effects of economic growth. Finally, the growth of the population, as well as the rise of energy intensity, can stimulate CO2 emissions in both local and neighboring regions. Industrialization seems to have a nonsignificant impact on emission changes due to the offsetting effects of the positive direct and negative indirect impacts of the share of secondary industry. Improvements in local urbanization may lead to an increase in emissions, while neighboring improvements may have stronger restricting effects; thus, urbanization improvement is beneficial to emissions reduction. This study provides more scientific information from both local and neighboring perspectives, which may differ from conventional results but still be beneficial for emissions reduction policy-makers to introduce corresponding measures.

Investigating the spatio-temporal influences of urbanization and other socioeconomic factors on city-level industrial NOx emissions: A case study in China

Cities play an essential role in industrial nitrogen oxides (NOx) control actions. Few studies have considered the spatiotemporal heterogeneity of the key influence factors that affect industrial NOx emissions at the city level. This work evaluates the impacts of urbanization level on industrial NOx emissions during 2017–2019 at the prefecture-city in China. We construct an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) framework based on the Geographically Weighted Regression (GWR), and Geographically and Temporally Weighted Regression (GTWR) model. The urbanization level was measured based on nighttime light (NTL) data. The results suggest that, first, the GTWR model has a better goodness of fit. Urbanization has a significant spatiotemporal heterogeneous effect on industrial NOx emissions. The median coefficient estimations of urbanization are −0.34 (95%CI: −1.38, 1.47), demonstrating a positive correlation in eastern China, yet negative in the western region. Other socioeconomic factors such as industrial electricity consumption, the share of secondary industry, population density, and research and development (R&D) expenditures have a significantly positive influence on industrial NOx emissions, while gross domestic production (GDP) has a negative impact. Our findings reveal that, due to the variances in urbanization levels and socio-economic factors, NOx control policies and regional emission reduction strategies in China implemented in a tailor-made form (such as “One-City-One-Policy”) will be more effective.

Exposure and Inequality of PM2.5 Pollution to Chinese Population: A Case Study of 31 Provincial Capital Cities from 2000 to 2016.

Fine particulate matter (PM2.5) exposure has been linked to numerous adverse health effects, with some disadvantaged subgroups bearing a disproportionate exposure burden. Few studies have been conducted to estimate the exposure and inequality of different subgroups due to a lack of adequate characterization of disparities in exposure to air pollutants in urban areas, and a mechanistic understanding of the causes of these exposure inequalities. Based on a long-term series of PM2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM2.5 in 31 provincial capital cities of China from 2000 to 2016 using the coefficient of variation and trend analyses. A health risk assessment of human exposure to PM2.5 from 2000 to 2016 was then undertaken. A cumulative population-weighted average concentration method was applied to investigate exposures and inequality for education level, job category, age, gender and income population subgroups. The relationships between socioeconomic factors and PM2.5 exposure concentrations were quantified using the geographically and temporally weighted regression model (GTWR). Results indicate that the PM2.5 concentrations in most of the capital cities in the study experienced an increasing trend at a rate of 0.98 μg m−3 per year from 2000 to 2016. The proportion of the population exposed to high PM2.5 (above 35 μg m−3) increased annually, mainly due to the increase of population migrating into north, east, south and central China. The higher educated, older, higher income and urban secondary industry share (SIS) subgroups suffered from the most significant environmental inequality, respectively. The per capita GDP, population size, and the share of the secondary industry played an essential role in unequal exposure to PM2.5.

Could green finance facilitate low-carbon transformation of power generation? Some evidence from China

PurposeThe decarbonization of power generation is key to achieving carbon neutrality in China by the end of 2060. This paper aims to examine how green finance influences China’s low-carbon transition of power generation. Using a provincial panel data set as an empirical study example, green finance is assessed first, then empirically analyses the influences of green finance on the low-carbon transition of power generation, as well as intermediary mechanisms at play. Finally, this paper makes relevant recommendations for peak carbon and carbon neutrality in China.Design/methodology/approachTo begin with, an evaluation index system with five indicators is constructed with entropy weighting method. Second, this paper uses the share of coal-fired power generation that takes in total power generation as an inverse indicator to measure the low-carbon transition in power generation. Finally, the authors perform generalized method of moments (GMM) econometric model to examine how green finance influences China’s low-carbon transition of power generation by taking advantage of 30 provincial panel data sets, spanning the period of 2007–2019. Meanwhile, the implementation of the 2016 Guidance on Green Finance is used as a turning point to address endogeneity using difference-in-difference method (DID).FindingsThe prosperity of green finance can markedly reduce the share of thermal power generation in total electricity generation, which implies a trend toward China’s low-carbon transformation in the power generation industry. Urbanization and R&D investment are driving forces influencing low-carbon transition, while economic development hinders the low-carbon transition. The conclusions remain robust after a series of tests such as the DID method, instrumental variable method and replacement indicators. Notably, the results of the mechanism analysis suggest that green finance contributes to low-carbon transformation in power generation by reducing secondary sectoral share, reducing the production of export products, promoting the advancement of green technologies and expanding the proportion of new installed capacity of renewable energy.Research limitations/implicationsThis paper puts forward relevant suggestions for promoting the green finance development with countermeasures such as allowing low interest rate for renewable energy power generation, facilitating market function and using carbon trade market. Additional policy implication is to promote high quality urbanization and increase R&D investment while pursuing high quality economic development. The last implication is to develop mechanism to strengthen the transformation of industrial structure, to promote high quality trade from high carbon manufactured products to low-carbon products, to stimulate more investment in green technology innovation and to accelerate the greening of installed structure in power generation industry.Originality/valueThis paper first attempts to examine the low-carbon transition in power generation from a new perspective of green finance. Second, this paper analyses the mechanism through several aspects: the share of secondary industry, the output of exported products, advances in green technology and the share of renewable energy in new installed capacity, which has not yet been done. Finally, this study constructs a system of indicators to evaluate green finance, including five indicators with entropy weighting method. In conclusion, this paper provides scientific references for sustainable development in China, and meanwhile for other developing countries with similar characteristics.

Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China.

Urban sprawl has become the main pattern of spatial expansion in many large cities in China, and its ecological and environmental effects profoundly impact Chinese urban development. In this paper, nighttime light data and statistical yearbook data are adopted as basic data sources to simulate the evolution trend of the urban sprawl in the upper Yangtze River (UYR), China. First, the urban sprawl index (USI) is employed to assess the level of urban sprawl and to determine the characteristics of urban sprawl under different scales. Second, the spatial autocorrelation model is applied to reveal the spatial pattern change characteristics of urban sprawl from 1992 to 2015. Third, a scenario analysis model of urban sprawl is constructed to simulate the evolution trend of the urban sprawl under different scenarios. Finally, based on the Geodetector, the influence of factors and factor interactions influencing urban sprawl in different time periods is analyzed. The results yield the following main conclusions: (1) The urban sprawl in the UYR first intensifies and then stabilizes over time. The number of cities with high USI in Sichuan province, medium cities, and Chengdu-Chongqing urban agglomeration increases over time, indicating that urban sprawl is intensifying in these areas. (2) The urban sprawl hot spots experience a pattern transformation process of point-like expansion-point-ring expansion-point-axis expansion-axis radiation. (3) Under the scenarios with different scales, the urban land sprawl in large cities is the highest, accounting for more than 47% of the UYR. Urban land sprawl extent in the Chengdu-Chongqing urban agglomeration is the highest, accounting for more than 51% of the UYR. The cities exhibiting the highest sprawl are Chongqing, Lijiang, and Kunming, accounting for 25.84%, 7.37%, and 5.11%, respectively, of the UYR. (4) In the different time scenario simulations, the urban land in large cities exhibits the highest sprawl, accounting for approximately 48.16% of the UYR. The urban land in the Chengdu-Chongqing urban agglomeration demonstrates the highest sprawl, accounting for 50.92% of the UYR. (5) From 1996 to 2002, the driver with the highest influence on urban sprawl was secondary industry share of GDP, with a q-statistic of 0.616. From 2009 to 2015, the driver with the highest influence on urban sprawl was green space per capita with a q-statistic of 0.396.

Determinants of Carbon Dioxide Emissions and Their Peaking Prospect: Evidence From China

In order to examine the key determinants of carbon dioxide emissions and judge whether China’s carbon dioxide emissions can reach their peak value before 2030, this study first uses the extended STIRPAT model to analyze the determinants of China’s carbon dioxide emissions from 1995 to 2019 and then uses the model regression result to forecast the carbon dioxide emissions from 2020 to 2040 under six scenarios to investigate their prospect. It is found that population size, GDP per capita, energy intensity, the share of coal consumption, urbanization level, the share of secondary industry, and investment have significant positive effects on carbon dioxide emissions. Among them, the influence of population size is the biggest and energy intensity is the weakest. China’s carbon dioxide emissions can reach their peak in 2029 under the baseline scenario. Increasing the rate of population growth, energy intensity, and share of coal consumption will push back the peak year. A lower rate of economic growth and share of the secondary industry will bring the peak year forward. Therefore, it is necessary to optimize the industrial structure and energy consumption structure, reduce the energy intensity, and control the population size in order to achieve the goal of peaking carbon dioxide emissions as soon as possible.

Relationship Between Energy Consumption and Industrial Structure

This paper analyses the differences in energy consumption by industry and studies the relationship between industrial structure and energy consumption in China. The findings of the study indicate that energy consumption is mostly concentrated in the sector Industry. Energy consumption for most Chinese provinces is greatly affected by the share of tertiary industry. In some provinces, however, the energy consumption tends to be influenced by the share of secondary industry. Depending on the specific situation, appropriate recommendations are proposed to each province.

Study on the Impact of Industrial Structure on Carbon Emissions in Sichuan Province -- Empirical Analysis Based on VAR Model

This paper analyses the dynamic relationship between industrial structure and carbon emissions in Sichuan Province using VAR models, using data on industrial structure and 8 carbon emissions of major energy consumption in Sichuan Province from 2001-2018. The results of the study show that in the short term, the advanced industrial structure will suppress the growth of carbon emissions, and the change in industrial structure, i.e., the increase in the share of secondary industry, will lead to an increase in carbon emissions; in the long term, the optimization of industrial structure has a significant impact on carbon emissions and specifically shows a suppressive effect. Therefore, in the short term, upgrading the industrial structure will not immediately reduce carbon emissions, but in the long term, adjusting the industrial structure is an important measure to reduce carbon emissions. In order to develop a low-carbon economy and accelerate the construction of a "clean energy demonstration province", Sichuan Province needs to optimise and upgrade its industrial structure, and specific measures are suggested, including: strengthening the role of regional radiation and promoting the balanced development of industries between regions; making full use of the province's human resources, increasing investment in research and development, and creating an important industrial cluster in China; strengthening the cooperation between Chengdu and Chongqing to promote the transformation and upgrading of the industrial structure.

The air quality changes and related mortality benefits during the coronavirus disease 2019 pandemic in China: Results from a nationwide forecasting study

Air quality changes during the coronavirus disease 2019 (COVID-19) pandemic in China has attracted increasing attention. However, more details in the changes, future air quality trends, and related death benefits on a national scale are still unclear. In this study, a total of 352 Chinese cities were included. We collected air pollutants (including fine particulate matter [PM2.5], inhalable particulate matter [PM10], nitrogen dioxide [NO2], and ozone [O3]) data for each city from January 2015 to July 2020. Convolutional neural network-quantile regression (CNN-QR) forecasting model was used to predict pollutants concentrations from February 2020 to January 2021 and the changes in air pollutants were compared. The relationships between the socioeconomic factors and the changes and the avoided mortality due to the changes were further estimated. We found sharp declines in all air pollutants from February 2020 to January 2021. Specifically, PM2.5, PM10, NO2, and O3 would drop by 3.86 μg/m3 (10.81%), 4.84 μg/m3 (7.65%), 0.55 μg/m3 (2.18%), and 3.14 μg/m3 (3.36%), respectively. The air quality changes were significantly related to many of the socioeconomic factors, including the size of built-up area, gross regional product, population density, gross regional product per capita, and secondary industry share. And the improved air quality would avoid a total of 7237 p.m.2.5-related deaths (95% confidence intervals [CI]: 4935, 9209), 9484 p.m.10-related deaths (95%CI: 5362, 13604), 4249 NO2-related deaths (95%CI: 3305, 5193), and 6424 O3-related deaths (95%CI: 3480, 9367), respectively. Our study shows that the interventions to control COVID-19 would improve air quality, which had significant relationships with some socioeconomic factors. Additionally, improved air quality would reduce the number of non-accidental deaths.

Anthropogenic factors of PM2.5 distributions in China’s major urban agglomerations: A spatial-temporal analysis

Fine particulate matter (PM2.5) poses a severe threat to public health in China. Understanding the PM2.5 distributions and relevant key drivers in major Chinese urban agglomerations by considering both spatial and temporal heterogeneities can provide an insight into formulating effective mitigation policies. In this study, we selected 12 urban agglomerations characterized by high levels of urbanization and severe PM2.5 pollution levels as our study area for the period of 1998–2016. Firstly, the indexes of GDP per capita and industrial structure were utilized to divide urban agglomerations into different urbanization stages. Then, advanced economic panel estimations (including the panel-corrected standard error (PCSE) method and feasible generalized least squares (FGLS) method) were employed to quantify the relationships between five anthropogenic factors and PM2.5 concentrations at the entire urbanization scale and the urbanization stage scale. As the results show, the urbanization processes of the 12 urban agglomerations studied can be classified into four different urbanization stages: the Primary Industrial Stage, Middle Industrial Stage, Late Industrial Stage, and Developed Stage. Averaged PM2.5 concentrations first underwent a rapid increase from the Primary Industrial Stage to Middle Industrial Stage and then decreased in the Late Industrial Stage and Developed Stage for most urban agglomerations. At the entire urbanization scale, GDP per capita (GDPP), population density (PD), and the share of secondary industry (IS) are major determinants of PM2.5 concentrations while the foreign direct investment (FDI) and energy intensity (EI) have a comparatively weaker influence. At the urbanization stage scale, GDPP generally made an initially positive and then negative contribution to PM2.5 concentrations throughout the urbanization process while IS and PD became the two main contributing factors as urban agglomerations entered the Late Industrial Stage and Developed Stage. PD significantly elevated PM2.5 levels in populous urban agglomerations throughout the urbanization stages. FDI and EI exhibit quite spatial heterogeneity across the Chinese urban agglomerations. Through a detailed analysis of the spatio-temporal quantification of PM2.5 distributions and of impacts from their determinants, this work contributes to a more thorough understanding of PM2.5 formation in Chinese key urban agglomerations and provides implications for regional joint prevention and control policies.

Air Quality Benefits From Implementing Best Available Techniques in Copper Mining and Smelting Complex Bor (Serbia)

Since 2016, the impact of the best available techniques (BAT) on air quality in Bor is significant. Its performance is presented by the air quality assessment with respect to the average annual sulfur dioxide (SO2) emitted concentrations and the total SO2 volume emitted per year from the smelter and sulfuric acid plant, relative to the quantity of processed concentrate in the period when production was conducted with the old technology, in 2013. Air quality in Bor was categorized as category III, i.e., the air was heavily polluted due to the exceeded tolerance of SO2 values. In the agglomeration Bor, during 2016/2017, air was of the category I, i.e., clean or slightly polluted air. The data illustrated that the temporal (yearly) variation of SO2 concentration was more influenced by human-related factors, especially by secondary industry share, as SO2 is mainly produced and discharged in the process of industrial production. The spatial variation of such concentration was more affected by meteorological indicators, especially by wind direction. In the period of the new technology, a significantly greater volume of concentrate was processed and a considerably larger quantity of cathode copper produced, and at the same time, total volume of emitted SO2 and measured heavy metals (lead-Pb, arsenic-As, cadmium-Cd, and nickel-Ni) was substantially reduced.

Temporal variations of six ambient criteria air pollutants from 2015 to 2018, their spatial distributions, health risks and relationships with socioeconomic factors during 2018 in China

Air pollution events occurred frequently in China, and tremendous efforts were devoted to the reduction of air pollution in recent years. Here, analysis of ambient monitoring data of six criteria air pollutants from 367 Chinese cities during 2015–2018, showed that PM2.5, PM10, SO2 and CO were reduced significantly by 22.1%, 13.5%, 46.4% and 21.5%, respectively, NO2 reduction was less significant (6.3%) while O3 level instead increased over China (13.7%). Spatial distribution, seasonal, monthly and diurnal variations of the air pollutants during 2018, implicated of effective control measures, were discussed in details, especially for the five key densely populated regions of Jing-Jin-Ji (JJJ), Fen Wei Plains (FWP), Yangtze River Delta (YRD), Sichuan Basin (SCB) and Pearl River Delta (PRD). Moreover, excess health risks (ERs) of the six pollutants were estimated for 2018, and such risks was two times higher if the World Health Organization (WHO) air quality guideline rather than Chinese guideline was adopted. PM10 rather than PM2.5 was the dominant contributor to ERs, and the case with both PM2.5 and PM10 exceeding threshold values occupied ~1/3 of total days, yet contributed ~2/3 of total ERs. For 2018, the health-risk based air quality index (HAQI) was further calculated by combining health risks from multiple pollutants, and it was found that high HAQI mostly distributed in North China Plain (NCP). ~15%, ~85% and ~95% people in YRD, FWP and JJJ were exposed to polluted air (HAQI > 100), and population-normalized HAQI further added the inequality, JJJ and a small region of SCB had much higher HAQI (>280). Investigations on HAQI with socioeconomic factors show that total population, population density and built-up area presented an inverted U-shape, suggesting existence of Environmental Kuznets Curve (EKC), while a positive relationship was found between HAQI and share of secondary industry. Multiple regression analysis suggested that built-up area was the most prominent factor to HAQI, followed by the gross domestic product (GDP). The findings here demonstrate in great details the current characteristics of air pollution and its associated health risks in China, therefore providing important implications for effective air pollution control strategies in near future for different regions of China.