Central Plains Urban Agglomeration Research Articles

Unraveling spatiotemporal patterns and multiple driving factors of surface ozone across China and its urban agglomerations management strategies

Since State Council launched the Action Plan for Air Pollution Prevention and Control in 2013, national concentration of fine particulate matter (PM2.5) has continued to decline in China, while surface ozone (O3) pollution shows an obvious rise. To identity hot regions and develop targeted policy, the spatiotemporal O3 variation and its population-weighted exposure features were analyzed in 337 cities across China, using autocorrelation analysis and grid exposure calculation. In the identified hot urban agglomerations, the correlation analysis and geographic weighted regression model (GWR) were used to study related meteorological factors and socioeconomic driving factors. O3 pollution and its human exposure were found to have significant spatial aggregation characteristics, showing a need for regional management policy. Beijing-Tianjin-Hebei Urban Agglomeration (BTH-UA), Central Plains Urban Agglomeration (CP-UA), and Yangtze River Delta Urban Agglomeration (YRD-UA) were identified as hot regions where O3 concentration exceeded 160 μg·m−3, exceedance rate was over 20% and population-weighted exposure risk was relatively high. Correlation analysis in the hot regions indicated high surface temperature, low relative humidity, and low wind speed were positive to O3 increase. Further, GWR results revealed that O3 in the majority of cities was positively related with population density (PD), the per capita GDP (Per_GDP), industrial soot emissions (ISE), industrial SO2 emissions (ISO2), and average annual concentration of inhaled fine particulate matter (PM10), and negatively related with total land area of administrative region (Administration) and area of green land (Green). From the regional driving factor difference, the targeted UA management policy was provided.

Spatial Heterogeneity analysis of urban forest ecosystem services in Zhengzhou City.

Understanding the spatial distribution of urban forest ecosystem services is essential for urban planners and managers to effectively manage cities and is an essential part of sustainable urban development. Mapping the spatial distribution of urban forest ecosystem services and improving the accuracy of its assessment scale will undoubtedly provide a more accurate reference basis for later management. In this study, we used the i-Tree Eco model and kriging interpolation to quantify and map urban forest ecosystem services and their spatial distribution in Zhengzhou, a city along the lower reaches of the Yellow River in China; analyzed the mapping errors and applicable conditions; and further explored the spatial differences using geographic probes. The i-Tree Eco model estimation results showed that the total carbon storage in the urban forest of Zhengzhou city was 75.7 tons, the annual carbon sequestration was 14.66 tons, the trees and shrubs in the urban area of Zhengzhou city could effectively avoid a total of 307.86 m3 of surface runoff per year, and trees and shrubs removed 411.8 kg/year of air pollution (O3, CO, NO2, PM2.5, PM10, and SO2). The spatial distribution of all urban forest ecosystem services showed significant heterogeneity, but the spatial evaluation precision of different factors varied. GDP and population data showed a negative correlation with ecosystem services, and ecosystem services were abundant in watershed and woodland areas. This study differs from traditional assessments based on regional data due to its improved spatial evaluation accuracy, and the results, discussion, and analysis not only help Zhengzhou's own urban development, but also provide a basis for the future construction and management of other cities, the Central Plains urban agglomeration, and the surrounding larger regions. This will contribute to the enhancement of ecosystem services and thus improve the ecological conditions of the region. This will also have a positive effect on the health of urban residents.

Impact assessment of urbanization on vegetation net primary productivity: A case study of the core development area in central plains urban agglomeration, China

Rapid urbanization process has a negative or positive impact on vegetation growth. Net primary productivity (NPP) is an effective indicator to characterize vegetation growth status. Taking the core development area of the Central Plains urban agglomeration as the study area, we estimated the NPP and its change trend in the past four decades using the Carnegie-Ames-Stanford Approach (CASA) model and statistical analysis based on meteorological and multi-source remote sensing data. Meanwhile, combined with the urbanization impact framework, we further analyzed urbanization's direct and indirect impact on NPP. The results showed that the urban area increased by 2688 km2 during a high-speed urbanization process from 1983 to 2019. As a result of the intense urbanization process, a continuous NPP decrease (direct impact) can be seen, which aggravated along with the acceleration of the urban expansion, and the mean value of direct impact was 130.84 g C·m−2·a−1. Meanwhile, urbanization also had a positive impact on NPP (indirect impact). The indirect impact showed an increasing trend during urbanization with a mean value of 10.91 g C·m−2·a−1. The indirect impact was mainly related to temperature in climatic factors. The indirect impact has a seasonal heterogeneity, and high-temperature environments of urban areas are more effective in promoting vegetation growth in autumn and winter than in summer. Among different cities, high-speed development cities have higher indirect impact values than medium's and low's because of better ecological construction. This study is of great significance for understanding the impact of urbanization on vegetation growth in the Central Plains urban agglomeration area, supporting urban greening plans, and building sustainable and resilient urban agglomerations.

Spatiotemporal Evolution Characteristics of PM2.5-O3 Compound Pollution in Chinese Cities from 2015 to 2020

Based on the monitoring data of PM2.5 and O3 concentrations in 333 cities in China from 2015 to 2020, using spatial clustering, trend analysis, and the geographical gravity model, this study quantitatively analyzed the characteristics of PM2.5-O3 compound pollution concentrations and its spatiotemporal dynamic evolution pattern in major cities in China. The results showed that:① there was a synergistic change in PM2.5 and O3 concentrations. When ρ(PM2.5_mean) ≤ 85 μg·m-3, for every 10 μg·m-3 increase in ρ(PM2.5_mean), the peak of the mean value of ρ(O3_perc90) increased by 9.98 μg·m-3. When ρ(PM2.5_mean) exceeded the national Grade II standards of (35±10) μg·m-3, the peak of the mean value of ρ(O3_perc90) increased the fastest, with an average growth rate of 11.81%. In the past six years, on average, 74.97% of Chinese cities with compound pollution had a ρ(PM2.5_mean) in the range of 45 to 85 μg·m-3. When ρ(PM2.5_mean)>85 μg·m-3, the mean value of ρ(O3_perc90) showed a significant decreased trend. ② The spatial clustering pattern of PM2.5 and O3 concentrations in Chinese cities was similar, and hot spots of the six-year mean values of ρ(PM2.5_mean) and ρ(O3_perc90) were distributed in the Beijing-Tianjin-Hebei urban agglomeration and other cities in the Shanxi, Henan, and Anhui provinces. ③ The number of cities with PM2.5-O3 compound pollution showed an interannual variation trend of increasing first (2015-2018) and then decreasing (2018-2020) and a seasonal trend of gradually decreasing from spring to winter. Further, the compound pollution phenomenon mainly occurred in the warm season (April to October). ④ The spatial distribution of PM2.5-O3 compound polluted cities was changing from dispersion to aggregation. From 2015 to 2017, the compound polluted areas spread from the eastern coastal areas to the central and western regions of China, and by 2017, a large-scale polluted area centered on the Beijing-Tianjin-Hebei urban agglomeration, the Central Plains urban agglomeration, and surrounding areas was formed. ⑤ The migration directions of PM2.5 and O3 concentration centers were similar, and there were obvious trends of moving westward and northward. The problem of high-concentration compound pollution was concentrated and highlighted in cities in central and northern China. In addition, since 2017, the distance between the centers of gravity of PM2.5 and O3 concentrations in the compound polluted areas had been significantly reduced, with a reduction of nearly 50%.

Analysis of Spatiotemporal Interaction Characteristics and Decoupling Effects of Urban Expansion in the Central Plains Urban Agglomeration

In recent years, the rapid improvement in the urbanization level of the Central Plains urban agglomeration is bound to bring about significant changes in urban land expansion and economic development. However, at present, there is little attention paid to the research on the spatiotemporal interaction characteristics of urban expansion and the interaction between urban expansion and economic development in this region, and existing research lacks a geographical analysis perspective. This study uses spatial autocorrelation, hot spot analysis, LISA time path, and standard deviation ellipse models to analyze the spatiotemporal interaction characteristics of urban expansion in the Central Plains urban agglomeration from 1990 to 2020, and it uses bilateral spatial autocorrelation and decoupling models to analyze the spatial correlation and decoupling effects of urban expansion and economic development. The results show that (1) the urban built-up area of the Central Plains urban agglomeration as a whole is growing in a “J” shape, and the expansion rate has increased rapidly in the past 10 years. (2) The spatial expansion of the city is mainly in the direction of “northwest–southeast”; the directionality has been gradually strengthened in the past 10 years, mainly in the direction of several prefecture-level cities under the jurisdiction of Anhui Province, and the spatial center of gravity of the city has shifted significantly to the south. (3) The spatial agglomeration characteristics of urban expansion in the Central Plains urban agglomeration are not obvious; local hot spots are concentrated in Jiaozuo and its surrounding areas, and urban expansion has local spatial structural instability. (4) During the 2005–2020 period, the risk of uncoordinated urban expansion and economic growth in the Central Plains urban agglomeration increased. This study is of great significance for the rational control of regional development, providing empirical reference for the formulation of the development planning of the Central Plains urban agglomeration, as well as providing a reference for research ideas and methods related to urbanization.

The category identification and transformation mechanism of rural regional function based on SOFM model: A case study of Central Plains Urban Agglomeration, China

Regional function identification and transformation mechanism exploration provide an entry point for a comprehensive understanding of the pattern and direction of rural development. This paper proposes a new analytical framework by establishing first, the rural production-living-ecological evaluation index; second, the optimal functional clustering of 186 counties in the Central Plains Urban Agglomeration area of China by combining the Self Organizing Feature Map network, Geodetector model, and Mann-Kendall test method; and third, the transformation mechanism by comparing the 2-phase zoning results with an interval of 10 years. An overall positive trend was found in the transformation of the rural function. The proportion of lagging development functional areas decreased by 20.5 %. The proportions of dominant function areas and composite coordination function areas increased by 5.4 % and 15.1 %, respectively. There was a synergistic relationship between production and living functions and an opposing relationship between ecological functions and the other two. The functional area with a synergistic relationship had the highest conversion rate, and most of the functional areas with trade-off relationships remained unchanged. The transfer-out rate of functional areas with synergistic and trade-off relations is negatively correlated with its lagging functional inferiority. The degree of difficulty in transitioning to functional areas with synergistic or trade-off relations is similar. The research reveals the mechanism and law of the evolution of rural regional functions and provides a methodology reference and policy planning basis for further research on rural areas.

Ozone exposure and health risks of different age structures in major urban agglomerations in People's Republic of China from 2013 to 2018.

High concentration of surface ozone (O3) will cause health risks to people. In order to analyze the spatiotemporal characteristics of O3 and assess O3 exposure and health risks for different age groups in China, we applied multiple methods including standard deviation ellipse, spatial autocorrelation, and exposure-response functions. Results show that O3 concentrations increased in 64.5% of areas in China from 2013 to 2018. The central plain urban agglomeration (CPU), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) witnessed the greatest incremental rates of O3 by 16.7%, 14.3%, and 13.1%. Spatially, the trend of O3 shows a significant positive autocorrelation, and high trend values primarily in central and east China. The proportion of the total population exposed to high O3 (above 160μg/m3) increased annually. Compared to 2013, the proportion of the young, adult, and old populations exposed to high O3 increased to different extents in 2018 by 26.8%, 29.6%, and 27.2%, respectively. The extent of population exposure risk areas in China expanded in size, particularly in north and east China. The total premature respiratory mortalities attributable to long-term O3 exposure in six urban agglomerations were about 177,000 in 2018 which has increased by 16.4% compared to that in 2013. Among different age groups, old people are more vulnerable to O3 pollution, so we need to strengthen their relevant health protection of them.

Study on the coupling and coordinated development of population, economy, technology and environment —Take Zhongyuan urban agglomeration as an example

In order to explore the internal relationship of the population - economy - science and technology - environment complex system, by constructing the evaluation index system of the population - economy - science and technology - environment complex system of the Central Plains urban agglomeration, using the combination weighting method of game theory to weight the indicators, and establishing an improved coupling coordination model, the development level of the population - economy - science and technology - environment of Zhongyuan urban agglomeration in 2010-2019 was analyzed, At the same time, the PSO optimized fractional grey prediction model is used to predict the period from 2020 to 2024.

Analysis of the PM2.5–O3 Pollution Characteristics and Its Potential Sources in Major Cities in the Central Plains Urban Agglomeration from 2014 to 2020

To highlight the characteristics of PM2.5–O3 pollution in the Central Plains Urban Agglomeration, spatial and temporal characteristics, key meteorological factors, and source pollution data for the area were analyzed. These data from the period 2014–2020 were obtained from state-controlled environmental monitoring stations in seven major cities of the agglomeration. The results revealed the following: (1) Spatially, the PM2.5–O3 pollution days were aggregated in the central area of Xinxiang and decreased toward the north and south. Temporally, during the 2014–2020 period, 50 days of PM2.5–O3 pollution were observed in the major cities of the Central Plains Urban Agglomeration, with an overall decreasing trend. (2) A low-temperature, high-pressure environment appeared unfavorable for the occurrence of PM2.5–O3 pollution days. Wind speeds of 2.14–2.19 m/s and a southerly direction increased the incidence of PM2.5–O3 pollution days. (3) The external transport range in summer was smaller and mainly originated from within Henan Province. These results can provide important reference information for achieving a synergistic control of PM2.5–O3 pollution, determining the meteorological causes, as well as the potential sources, of PM2.5–O3 pollution in polluted areas and promoting air pollution control.

Impact of High-Speed Rail on Spatial Structure in Prefecture-Level Cities: Evidence from the Central Plains Urban Agglomeration, China

The impact of high-speed rail (HSR) on urban spatial structure has attracted much attention since the 1970s. It mainly realizes the change of urban spatial structure by affecting the spatial distribution of population and economy. Based on population and industry data on 29 cities in the Central Plains Urban Agglomeration (CPUA) located in central China during 2005–2017, in this paper difference-in-difference (DID) models are utilized to explore the influence of HSR on the spatial structure of prefecture-level cities at the single/multicenter levels according to the dimensions of polycentricity and dispersion. The main conclusions are shown as: (1) HSR on the spatial structure of the CPUA has a monocentric trend, but the characteristics of different prefecture-level cities vary. Currently, agglomeration remains the dominant force of the spatial structure of the CPUA in prefecture-level cities. (2) HSR have a noticeable effect on the spatial structure of industry, whereas the influences of the HSR on the spatial structure of the population are insignificant. Its development is the result of the combined effects of many factors, including natural factors, socioeconomic factors, policies, and transportation factors. Among the controlling factors, the administrative area, economic development level, urban population, and number of research units are the critical factors having a hold on the population spatial structure of the CPUA.

PM2.5 Pollution in Six Major Chinese Urban Agglomerations: Spatiotemporal Variations, Health Impacts, and the Relationships with Meteorological Conditions

To investigate the spatiotemporal patterns of fine particulate matter (PM2.5) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM2.5 concentrations data during the period of 2017–2020 in six typical major urban agglomerations. During the period of 2017–2020, PM2.5 concentrations in the Beijing–Tianjin–Hebei urban agglomeration (BTH-UA), Central Plains urban agglomeration (CP-UA), Yangtze River Delta urban agglomeration (YRD-UA), Triangle of Central China urban agglomeration (TC-UA), Chengdu–Chongqing urban agglomeration (CY-UA), and Pearl River Delta urban agglomeration (PRD-UA) decreased at a rate of 6.69, 5.57, 5.45, 3.85, 4.66, and 4.1 µg/m3/year, respectively. PM2.5 concentration in BTH-UA decreased by 30.5% over four years, with an annual average of 44.6 µg/m3 in 2020. CP-UA showed the lowest reduction ratio (22.1%) among the six regions, making it the most polluted urban agglomeration. In southern BTH-UA, northeastern CP-UA, and northwestern TC-UA, PM2.5 concentrations with high levels formed a high–high agglomeration, indicating pollution caused by source emission in these areas was high and hard to control. Atmospheric temperature, pressure, and wind speed have important influences on PM2.5 concentrations. RH has a positive correlation with PM2.5 concentration in north China but a negative correlation in south China. We estimated that meteorological conditions can explain 16.7–63.9% of the PM2.5 changes in 129 cities, with an average of 33.4%, indicating other factors including anthropogenic emissions dominated the PM2.5 changes. Among the six urban agglomerations, PM2.5 concentrations in the CP-UA were most influenced by the meteorological change. Benefiting from the reduction in PM2.5 concentration, the total respiratory premature mortalities in six regions decreased by 73.1%, from 2017 to 2020. The CP-UA had the highest respiratory premature mortality in six urban agglomerations. We suggested that the CP-UA needs more attention and stricter pollution control measures.

Carbon footprint and embodied carbon transfer at city level: A nested MRIO analysis of Central Plain urban agglomeration in China

With the establishment of several carbon peak and carbon neutrality strategies in China, urban carbon mitigation has become a critical issue. Understanding how cities obtain, distribute, and manage their carbon emissions could be an essential step for collaborative management of regional carbon emission reduction. In the present study, by compiling a nested multi-regional input-output table, an investigation was conducted into the city-level carbon footprints and inter-regional CO2 transfer embodied in the trade of finished goods at home and abroad for 29 cities in Central Plain urban agglomeration. The total carbon footprint of the urban agglomeration was found to be 1144.6 Mt, accounting for 10% of the national emissions, and significant differences were observed in the city-level carbon footprints, ranging from 3.2 Mt (Huaibei) to 176.3 Mt (Zhengzhou). The carbon footprint per capita also varied greatly, from Huaibei (1.4 t) to Jiyuan (20.7 t), and was significantly correlated with GDP per capita. CO2 emissions due to capital formation contributed the most to the carbon footprint, followed by urban consumption. Through the export of numerous industrial products to support infrastructure construction and economic activities, the urban agglomeration received 736.3 Mt net emissions from other regions at home and abroad. Significant embodied CO2 transfer was observed to drift from energy-abundant cities, such as Pingdingshan and Shangqiu, to richer cities, such as Zhengzhou and Luoyang. The inter-city CO2 flows were mainly shaped by heavy industry, construction and service sectors. Finally, targeted mitigation policy methods have been recommended for different types of cities from production and consumption perspectives.

Spatial Expansion and Correlation of Urban Agglomeration in the Yellow River Basin Based on Multi-Source Nighttime Light Data

The Chinese government proposed a major national strategy for ecological protection and high-quality development in the Yellow River Basin. The Framework of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin proposes building a dynamic development pattern characterized by “one axis, two regions and five poles” in the Yellow River Basin with high-quality and high-standard urban agglomerations along the Yellow River. The urban agglomeration is the economic growth pole of the Yellow River Basin and the main carrier of the population and productivity. This study integrates DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night light remote sensing data from 2000 to 2020 and uses methods such as spatial expansion measurement, the center of gravity offset, urban primacy, and the gravity model to study the spatial expansion and correlation characteristics of five urban agglomerations. The results show that: (1) From 2000 to 2020, urban agglomeration in the Yellow River Basin continued to expand, and the area increased by 6.4 times. The total amount of nighttime lights in the city presents a spatial distribution pattern that is high in the east and low in the west. (2) The expansion centers of the five major urban agglomerations all shifted. The centers of gravity of the Shandong Peninsula urban agglomeration, the Jiziwan urban agglomeration of the Yellow River, the Guanzhong Plain urban agglomeration, and the Lanzhou–Xining urban agglomeration all shifted westward, while the center of gravity of the Central Plains urban agglomeration shifted to the southeast. (3) Qingdao, Zhengzhou, Xi’an and Lanzhou are the primate cities of the four urban agglomerations of the Shandong Peninsula, Central Plains, Guanzhong Plain, and Lanzhou–Xining, respectively. The primate city in the Jiziwan urban agglomeration of the Yellow River was changed from Taiyuan to Yinchuan and then to Yulin. (4) The density of the gravitational network of the urban agglomeration in the Yellow River Basin and the distribution of the maximum gravitational line show the spatial differentiation characteristics of being dense in the east and sparse in the west.

Simulating the Spatial Mismatch between Ecosystem Services’ (ESs’) Supply and Demand Based on Their Spatial Transfer in Urban Agglomeration Area, China

Ecosystem service spatial transfer is considered a feature that can deliver ecosystem services at a distance to meet the demands in areas with uneven spatial distribution of natural and social economic development. The natural ES spatial transfer distance and intensity were simulated by using the modified breaking point model in the Central Plains urban agglomeration (CPUA) with the cities of Luoyang, Zhengzhou, Shangqiu, and Huaibei stretching across. It is shown that there is a spatial mismatch between ES supply from ecospace and its demands from cities; relying only on natural spatial transfer, none of the ESs of the ecospace can be transported to corresponding population centers; and a spatial gap between ES supply and demand is illustrated in urban agglomeration areas. Intercity cooperation in ecosystem management and landscape planning based on ES spatial transfer would be good choices for cities, giving full play to comparative advantages to achieve sustainable development for the entire CPUA.

Polycyclic aromatic hydrocarbons in soils of Central Plains Urban Agglomeration, China: The bidirectional effects of urbanization and anthropogenic activities

To investigate the variations in environmental behavior (levels, distribution, sources, and soil toxicity) of polycyclic aromatic hydrocarbons (PAHs) under the impact of anthropogenic activities during the urbanization process, we collected soil samples from 195 sites in the Central Plains Urban Agglomeration (CPUA), North China, and analyzed 16 U.S. Environmental Protection Agency (EPA) PAH priority pollutants. We divided the sampling sites into three groups (urban area, industrial area, and farmland) and collected soil samples (0–20 cm surface layer). ∑16PAHs concentrations in the soils of the urban area, industrial area, and farmland ranged from 24.2 to 4400 ng/g, 12.3–8780 ng/g, and 20.9–852 ng/g (the average value of 349, 634, and 186 ng/g), respectively. The 4 to 5 ring PAHs were dominant compounds in three soil types, accounting for 65–80% of the ∑16PAHs. The results of the source analysis showed that the PAHs in the soils of CPUA were mainly from energy consumption. PAH levels in urban and industrial soils had a potential low cancer risk. The impact of urbanization on PAHs in the soil was bidirectional. On the one hand, the level of PAHs in the farmland soil might increase due to burning coal and agricultural machinery, which releases diesel or petrol fumes. On the other hand, in the urbanization process, the PAH content in urban soil and industrial soil showed a downward trend due to the implementation of environmental protection policies in China, which have reduced the atmospheric input of PAHs into the soil.

Spatiotemporal variations and sources of PM2.5 in the Central Plains Urban Agglomeration, China.

The Central Plains Urban Agglomeration (CPUA) is the largest region in central China and suffers from serious air pollution. To reveal the spatiotemporal variations and the sources of fine particulate matter (PM2.5, with an aerodynamic diameter of smaller than 2.5 μm) concentrations of CPUA, multiple and transdisciplinary methods were used to analyse the collected millions of PM2.5 concentration data. The results showed that during 2017 ~ 2020, the yearly mean concentrations of PM2.5 for CPUA were 68.3, 61.5, 58.7, and 51.5 μg/m3, respectively. The empirical orthogonal function (EOF) analysis suggested that high PM2.5 pollution mainly occurred in winter (100.8 μg/m3, 4-year average). The diurnal change in PM2.5 concentrations varied slightly over the season. The centroid of the PM2.5 concentration moved towards the west over time. The spatial autocorrelation analysis indicated that PM2.5 concentrations exhibited a positive spatial autocorrelation in CPUA. The most polluted cities distributed in the northern CPUA (Handan was the centre) formed a high-high agglomeration, and the cities located in the southern CPUA (Xinyang was the centre) formed a low-low agglomeration. The backward trajectory model and potential source contribution function were employed to discuss the regional transportation of PM2.5. The results demonstrated that internal-region and cross-regional transport of anthropogenic emissions were all important to PM2.5 pollution of CPUA. Our study suggests that joint efforts across cities and regions are necessary.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11869-022-01178-z.

How does urban environmental legislation guide the green transition of enterprises? Based on the perspective of enterprises' green total factor productivity

This paper uses a dynamic slack-based measure (DSBM) model to calculate an enterprise's green total factor productivity (GTFP), used as the proxy variable for measuring the enterprise's green transition. We use urban environmental legislation as a quasi-natural experiment and introduce it into a partial equilibrium analysis framework for heterogeneous enterprises and theoretically reveal the mechanism in how urban environmental legislation affects enterprises' GTFP. The results show that implementing urban environmental legislation can significantly improve the GTFP of enterprises. This conclusion remains robust to multiple scenarios. Our heterogeneity analyses show that urban environmental legislation has a relatively significant impact on non–state-owned enterprises, enterprises with low financing constraints, enterprises located in Two Control Zones and Central Plains Urban Agglomeration. Compared with the ex-post-facto treatment of pollutants through abatement, encouraging enterprises to increase investment in prevention is more conducive to improving enterprise GTFP. Further inspection shows that environmental legislation on the prevention of water, air, and solid waste pollutants has a greater impact on enterprise GTFP. In addition, this paper uses a simhash algorithm to quantify the 662 laws, finding that the improvement effect on enterprise GTFP is more significant with increasing intensity in urban environmental legislation.

Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015\u20132019

The "comparative attitude" of urban agglomerations involves multidimensional perspectives such as infrastructure, ecological protection, and air pollution. Based on monitoring station data, comparative studies of multispatial, multitimescale and multiemission pollution sources of air quality on 19 urban agglomerations during the 13th Five-Year Plan period in China were explored by mathematical statistics. The comparison results are all visualized and show that clean air days gradually increased and occurred mainly in summer, especially in South and Southwest China. PM2.5, PM10 and O3 were still the main primary pollutants. PM2.5 is mainly concentrated in December, January and February, and PM10 is mainly concentrated in October–November and March–April. The O3 pollution in the Pearl River Delta and Beibu Gulf urban agglomerations located in the south is mainly concentrated from August to November, which is different from others from May to September. Second, from 2015 to 2019, the increasing rate of O3 concentration in any hour is higher than that of particulate matter (PM). Diurnal trends in O3 concentration in all directions also showed a single peak, with the largest increments that appeared between 13:00 and 16:00, while the spatial distribution of this peak was significantly regional, earlier in the east but later in the west. Third, this analysis indicated that the annual average air quality index (AQI) showed a gradually decreasing trend outward, taking the Central Plain urban agglomeration as the center. The ambient air pollutants are gradually moving southward and mainly concentrated in the Central Plains urban agglomeration from 2015 to 2019. Furthermore, in each urban agglomeration, the cumulative emission of PM2.5 is consisted of the four average emissions, which is approximately 2.5 times of that of PM10, and industries are the main sources of PM2.5, PM10 and VOCs (volatile organic compounds). VOCs and NOX increased in half of the urban agglomerations, which are the reasons for the increase in ozone pollution. The outcomes of this study will provide targeted insights on pollution prevention in urban agglomerations in the future.

Impact Assessment of Urbanization on Vegetation Net Primary Productivity: A Case Study of the Core Development Area in Central Plains Urban Agglomeration, China

Impact Assessment of Urbanization on Vegetation Net Primary Productivity: A Case Study of the Core Development Area in Central Plains Urban Agglomeration, China

Source apportionment of environmentally persistent free radicals (EPFRs) and heavy metals in size fractions of urban arterial road dust

The partitioning of pollutants in the size-fractions of road dust particles is extremely important to their migration and bioavailability in the air environment. Herein, the pollution characteristics and sources of heavy metals (As, Cd, Cu, Cr, Pb, Zn, Co, Mn, Ni) and environmentally persistent free radicals (EPFRs) are investigated in 64 samples of mixed particle-size road dust in four size fractions (>250 µm, 150–250 µm, 75–150 µm,<75 µm). The samples were collected from urban arterial roads of three cities in the core of Central Plains Urban Agglomeration. The results indicate that 67.70% of the road dust (by mass) consist of the<75 µm particles size fraction, and that the highest levels of EPFRs and heavy metals are concentrated in the<150 µm and< 75 µm size fractions. In addition, Cd exhibits extremely high average enrichment factors (EFs) of 51.84 in the 75–150 µm fraction and 64.80 in the<75 µm fraction. Further analysis reveals a remarkable correlation between EPFR concentration and the concentrations Zn and Cr in the road dust. An application of principal component analysis (PCA) and positive matrix factorization (PMF) to the regional investigation and the layout of functional zones demonstrates that Cr, Pb, Zn and EPFRs are mainly derived from traffic emissions, and account for 51.45–77.86% of the fine particle fraction (<150 µm). Meanwhile, industrial activities are identified as the primary source of Cd and Cu, accounting for 63.07–76.22% of the<150 µm particle size fraction. These findings suggest a potential risk of EPFRs and heavy metals in fine particulate matter, and provide a new insight into the potential sources and health risks of EPFRs in road dust. Therefore, further studies are needed in order to explore the EPFRs in fine particulate particles and to assess their effects upon human health.