Analysis of air quality characteristics of Beijing-Tianjin-Hebei and its surrounding air pollution transport channel cities in China

Urban design and pollution using AI: Implications for urban development in China

The primary aim of this study is to explore the role of AI in urban design and its potential to reduce pollution in Chinese cities. The study investigates how AI-driven urban planning tools can be applied to create more sustainable, efficient, and functional urban environments. PM2.5 and PM10 show high concentrations with peaks between 2014 and 2017, indicating simple pollution actions. Post-2017, there is a noticeable decline in pollution levels, possibly due to improved regulations or the global impact of the COVID-19 pandemic. Specific years, like 2016, show extreme spikes, possibly due to industrial activities or natural events. The overall trend suggests improved air quality and moderate to strong positive correlations exist between PM2.5 and PM10, NO2, SO2, and CO, indicating shared bases or co-occurrence. However, there is no significant correlation between PM2.5 and O3, suggesting different bases and behaviors. Bi-directional causality is observed between PM2.5 and PM10, PM2.5 and O3, PM2.5 and NO2, PM2.5 and SO2, and PM2.5 and CO. This mutual cause suggests interrelated impressive processes and shared bases. The results of the causality analysis suggest the existence of complex interactions, where high levels of pollution can predict changes in others. AI in urban design play vital role for identifying the most effective strategies for reducing pollution and helping to build more sustainable and functional urban environments in China.

Quantifying the scale-dependent relationships of PM2.5 and O3 on meteorological factors and their influencing factors in the Beijing-Tianjin-Hebei region and surrounding areas

To investigate the variations of PM2.5 and O3 and their synergistic effects with influencing factors at different time scales, we employed Bayesian estimator of abrupt seasonal and trend change to analyze the nonlinear variation process of PM2.5 and O3. Wavelet coherence and multiple wavelet coherence were utilized to quantify the coupling oscillation relationships of PM2.5 and O3 on single/multiple meteorological factors in the time-frequency domain. Furthermore, we combined this analysis with the partial wavelet coherence to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationships. The results obtained from this comprehensive analysis are as follows: (1) The seasonal component of PM2.5 exhibited a change point, which was most likely to occur in January 2017. The trend component showed a discontinuous decline and had a change point, which was most likely to appear in February 2017. The seasonal component of O3 did not exhibit a change point, while the trend component showed a discontinuous rise with two change points, which were most likely to occur in July 2018 and May 2017. (2) The phase and coherence relationships of PM2.5 and O3 on meteorological factors varied across different time scales. Stable phase relationships were observed on both small- and large-time scales, whereas no stable phase relationship was formed on medium scales. On all-time scales, sunshine duration was the best single variable for explaining PM2.5 variations and precipitation was the best single variable explaining O3 variations. When compared to single meteorological factors, the combination of multiple meteorological factors significantly improved the ability to explain variations in PM2.5 and O3 on small-time scales. (3) Atmospheric teleconnection factors were important driving factors affecting the response relationships of PM2.5 and O3 on meteorological factors and they had greater impact on the relationship at medium-time scales compared to small- and large-time scales.

Spatiotemporal characteristics of air pollutants and their associated health risks in '2+26' cities in China during 2016-2020 heating seasons

To understand characteristics of air pollutants and their associated health risks in recent heating seasons in China, ambient monitoring data of six air pollutants in '2 + 26' cities in Beijing-Tianjin-Hebei and its surrounding areas (known as the BTH2+26 cities) during 2016-2020 heating seasons was analyzed. Results show that daily average concentrations of PM2.5, PM10, SO2, NO2, and CO dropped significantly in BTH2+26 cities from the 2016-2017 heating season to 2019-2020 heating season, while 8h O3 increased markedly. During 2016-2020 heating seasons, annual average values of total excess risks (ERtotal) were 2.3% mainly contributed by PM2.5 (54.4%) and PM10 (36.1%). With PM2.5 pollution worsening, PM10 and NO2 were the important contribution factors of the enhanced ERtotal. Higher health-risk based air quality index (HAQI) values were mainly concentrated in the western Hebei and northern Henan. HAQI showed spatial agglomeration effect in four heating seasons. Impact factors of HAQI varied in different heating seasons. These findings can provide useful insights for China to further propose effective control strategies to alleviate air pollution in the future.

Distinct urban-rural gradients of air NO2 and SO2 concentrations in response to emission reductions during 2015-2022 in Beijing, China

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are two major air pollutants in urban environment. Emission reduction policies have thus been implemented to improve urban air quality, especially in the metropolises. However, it remains unclear whether the air concentrations of NO2 and SO2 in and around large cities follow a same spatial pattern and how their characteristics change over time in response to the emission reductions. Using ground-based monitoring datasets of air NO2 and SO2 concentrations in Beijing, China, we tested the hypothesis of urban air pollutant islands and evaluated their seasonal and inter-annual variations during 2015-2022. The results showed that air NO2 concentrations increased significantly towards the urban core, being in line with the hypothesis of urban air pollutant island, while air SO2 concentrations showed no such spatial patterns. The urban air NO2 island varied seasonally, with larger radius and higher air NO2 concentrations in spring and winter. In response to the emission reduction, the annual mean radius of the urban air NO2 island showed a rapid decrease from 45.8 km to zero km during the study period. The annual mean air NO2 concentration at the urban core showed a linear decrease at a rate of 4.5 μg m-3 yr-1. In contrast, air SO2 concentration decreased nonlinearly over time and showed a legacy in comparison to the emission reduction. Our findings suggest different urban-rural gradients of air NO2 and SO2 concentrations and highlight their distinct responses to the regional reductions of anthropogenic emissions.

Spatiotemporal variations of NO2 and its driving factors in the coastal ports of China

Nitrogen Dioxide (NO₂) is one of the major air pollutants in coastal ports of China. Understanding the spatiotemporal varying effects of driving factors of NO₂ is vital for the implementation of differentiated air pollution control measures for different port areas. Based on the Ozone Monitoring Instrument (OMI) satellite data, we adopted a Geographically and Temporally Weighted Regression (GTWR) model to explore the influences of meteorological and socioeconomic factors on the NO₂ Vertical Column Concentrations (VCDs) in coastal ports of China from 2015 to 2021. The results indicate that NO₂ VCD in most ports has decreased since 2016 and the ports with serious NO₂ pollution are mainly distributed in northern China. The associations between NO₂ VCD levels and their drivers exhibit obvious spatiotemporal heterogeneity. Higher wind speed and relative humidity are more helpful to alleviate NO₂ pollution in ports of the Bohai Rim and the Pearl River Delta. Cargo throughput has more closely associated with NO₂ pollution in Beibu Gulf in recent years, yet there is no significant association found for Shanghai ports. The positive relationship between transportation emissions and NO₂ VCD is more significant in southern ports. This work provides some implications for the formulation of targeted emission reduction policies for different ports along the Chinese coast.

Regional characteristics and spatial correlation of haze pollution: Interpretative system analysis in cities of Fenwei Plain in China

Urban agglomeration is an important model for promoting global economic development and has made important contributions to global economic integration. However, as the core area of urbanization and industrialization, urban agglomerations also contribute to air pollutant emissions primarily due to the agglomeration of population and industry. The mutual influence of air pollution between different cities in urban agglomerations has brought significant challenges to global environmental governance. The Fenwei Plain is one of the most severely polluted areas in China. We collected daily average PM_2.5 concentration data of 11 cities in the Fenwei Plain, China in 2019. We then developed an interpretive structural model to statistically analyze the spatial correlation and hierarchical transmission of haze pollution between the 11 cities. The results showed that haze pollution has a strong systematic correlation between the 11 cities, and a regional haze pollution community has formed throughout the region. Haze pollution also exhibits evident transmission and spatial correlations between the cities. The transmission starts from Baoji and ends at Sanmenxia, with mutual interactions between the cities of Xi'an, Xianyang, Weinan, Tongchuan, Jinzhong, Lvliang, Linfen, Yuncheng, and Luoyang. Thus, air pollution prevention and control in the Fenwei Plain should consider the spatial correlation of haze pollution between different cities, especially in autumn and winter, and should rationally be implemented in key urban cluster areas. We recommend building a coordinated governance between cities to improve the overall air quality. Our findings shed a light for coordinated pollution management in urban agglomerations worldwide.

The diminishing effects of winter heating on air quality in northern China

Cleaner winter heating has been promoted to abate the winter air pollution in northern China. Although improvements in air quality have been observed, the effectiveness and mechanism of cleaner heating measures on air quality have not been examined on the empirical ground. In this study, we estimate the annual effects of winter heating policy on air quality from 2014 to 2017 using a regression discontinuity design (RDD) and dynamic regression model. The results show that winter heating aggravates Air Quality Index (AQI). Specifically, the AQI raised by winter heating reduce from 85.3 in 2014 to 24.1 in 2017, indicating diminishing effects of winter heating with the implementation of clean heating measures. The heterogeneous characteristics of winter heating in terms of different pollutants and city scales are further quantified. The effects of clean heating are more evident for particulate pollutants (PM_2.5 and PM₁₀) than for SO₂, NO₂, CO and O₃. The promotion of clean heating is more effective in larger cities. These findings provided insights into the diminishing air pollution change with continuous advancement in clean heating policy and the heterogeneity among cities and pollutants should be taken into account when formulating future policies in response to energy transition and climate change.

Advances in coastal ocean boundary layer detection technology and equipment in China

Accurate and comprehensive knowledge of the atmospheric environment and its evolution within the coastal ocean boundary layer are necessary for understanding the sources, chemical mechanisms, and transport processes of air pollution in land, sea, and atmosphere. We present an overview of coastal ocean boundary layer detection technology and equipment in China and summarize the progress and main achievements in recent years. China has developed a series of coastal ocean boundary layer detection technologies, including Light Detection and Ranging (LIDAR), turbulent exchange analyzer, air-sea flux analyzer, stereoscopic remote sensing of air pollutants, and oceanic aerosol detection equipment to address the technical bottleneck caused by harsh environmental conditions in coastal ocean regions. Advances in these technologies and equipment have provided scientific assistance for addressing air pollution issues and understanding land-sea-atmosphere interactions over coastal ocean regions in China. In the future, routine atmospheric observations should cover the coastal ocean boundary layer of China.

Payments for environmental services strategy for transboundary air pollution: A stochastic differential game perspective

Air pollution has become a global threat to societal development. The main challenges of transboundary air pollution control include two perspectives: uneven socioeconomic development of regions and the diffusion of air pollution. This paper proposes an PES strategy to alleviate transboundary air pollution by coordinating regional economic interests and environmental preferences within the joint prevention and control of air pollution region. To make the model design more realistic, we introduce the stochastic differential game model to characterize the diffusion and uncertainty of air pollution. The optimal feedback Nash equilibrium is derived in three PES scenarios (no PES, dynamic PES, and fixed-fee PES) by using the Hamilton-Jacobi-Bellman equation. Numerical simulations and sensitivity analysis are implemented to compare the optimal strategies under the three PES scenarios. The dynamic PES strategy is shown to outperform the no PES strategy and the fixed-fee PES strategy by encouraging the backward region to cut more emissions. Besides, the confidence interval theory is used to estimate the variation range of air pollution stocks, which provides a powerful diagnostic tool for policy-makers.

Variation of Aerosol Optical Depth Measured by Sun Photometer at a Rural Site near Beijing during the 2017–2019 Period

In recent years, the Beijing–Tianjin–Hebei region has become one of the worst areas for haze pollution in China. Sun photometers are widely used for aerosol optical property monitoring due to the advantages of fully automatic acquisition, simple maintenance, standardization of data processing, and low uncertainty. Research sites are mostly concentrated in cities, while the long-term analysis of aerosol optical depth (AOD) for the pollution transmission channel in rural Beijing is still lacking. Here, we obtained an AOD monitoring dataset from August 2017 to March 2019 using the ground-based CE-318 sun photometer at the Gucheng meteorological observation site in southwest Beijing. These sun photometer AOD data were used for the ground-based validation of MODIS (Moderate Resolution Imaging Spectroradiometer) and AHI (Advanced Himawari Imager) AOD data. It was found that MODIS and AHI can reflect AOD variation trends by sun photometer on daily, monthly, and seasonal scales. The original AOD measurements of the sun photometer show good correlations with satellite observations by MODIS (R = 0.97), and AHI (R = 0.89), respectively, corresponding to their different optimal spatial and temporal windows for matching with collocated satellite ground pixels. However, MODIS is less stable for aerosols of different concentrations and particle sizes. Most of the linear regression intercepts between the satellite and the photometer are less than 0.1, indicating that the errors due to surface reflectance in the inversion are small, and the slope is least biased (AHI: slope = 0.91, MODIS: slope = 0.18) in the noon period (11 a.m.–2 p.m.) and most biased in summer (AHI: slope = 0.77, MODIS: slope = 1.31), probably due to errors in the aerosol model. The daily and seasonal variation trends between CE-318 AOD measurements in the Gucheng site and fine particulate observations from the national air quality site nearby were also compared and investigated. In addition, a typical haze–dust complex pollution event in North China was analyzed and the changes in AOD during the pollution event were quantified. In processing, we use sun photometer and satellite AOD data in combination with meteorological and PM data. Overall, this paper has implications for the study of AOD evolution patterns at different time scales, the association between PM2.5 concentrations and AOD changes, and pollution monitoring.

Acute effect of air pollutants' peak-hour concentrations on ischemic stroke hospital admissions among hypertension patients in Beijing, China, from 2014 to 2018

Air pollutants' effect on ischemic stroke (IS) has been widely reported. But the effect of high-level concentrations during people's outdoor periods among hypertension patients was unknown. Peak-hour concentrations were defined considering air pollutants' high concentrations as well as people's outdoor periods. We conducted a time-series study and used the generalized additive model to analyze peak-hour concentrations' acute effect. A total of 315,499 IS patients comorbid with hypertension were admitted to secondary and above hospitals in Beijing from 2014 to 2018. A 10 µg/m³ (CO: 1 mg/m³) increase of the peak-hour concentrations was positively associated with IS hospital admissions among hypertension patients. The maximum effect sizes were as follows: for PM_2.5, 0.17% (95% confidence interval [CI]: 0.10-0.24%) at Lag0 and 0.22% (95% CI: 0.12-0.33%) at Lag0-5; for PM₁₀, 0.09% (95% CI: 0.05-0.13%) at Lag5 and 0.17% (95% CI: 0.09-0.26%) at Lag0-5; for SO₂, 0.87% (95% CI: 0.46-1.29%) at Lag5; for NO₂, 0.83% (95% CI: 0.62-1.04%) at Lag0 and 0.86% (95% CI: 0.59-1.13%) at Lag0-1; for CO 1.23% (95% CI: 0.66-1.80%) at Lag0 and 1.33% (95% CI: 0.33-2.35%) at Lag0-5; for O₃ 0.23% (95% CI: 0.12-0.35%) at Lag0 and 0.20% (95% CI: 0.05-0.34%) at Lag0-1. The effect sizes of PM_2.5, NO₂, and O₃ remained significant after adjusting daily mean. Larger effect sizes were observed for PM_2.5 and PM₁₀ in cool season and for O₃ in warm season. As significant exposure indicators of air pollution, peak-hour concentrations exposure increased the risk of IS hospital admissions among hypertension patients and it is worthy of consideration in relative environmental standard. It is suggested for hypertension patients to avoid outdoor activity during peak hours. More relevant searches are required to further illustrate air pollutant's effect on chronic disease population.

Impact of Particulate Matter on Hospitalizations for Respiratory Diseases and Related Economic Losses in Wuhan, China

Background

Prior studies have reported the effects of particulate matter (PM) on respiratory disease (RD) hospitalizations, but few have quantified PM-related economic loss in the central region of China. This investigation aimed to assess the impacts of PM pollution on the risk burden and economic loss of patients admitted with RD.

Methods

Daily cases of RD admitted to the hospital from 1 January 2015 to 31 December 2020 were collected from two class-A tertiary hospitals in Wuhan, China. Time series analysis incorporated with a generalized additive model (GAM) was adopted to assess the impacts of fine particulate matter (PM_2.5) and inhalable particulate matter (PM₁₀) exposures on patients hospitalized with RD. Stratified analyses were performed to investigate underlying effect modification of RD risk by sex, age, and season. The cost of illness (COI) approach was applied to evaluate the related economic losses caused by PM.

Results

A total of 51,676 inpatients with a primary diagnosis of RD were included for the analysis. PM_2.5 and PM₁₀ exposures were associated with increased risks of hospitalizations for RD. Subgroup analysis demonstrated that men and children in the 0–14 years age group were more vulnerable to PM, and the adverse effects were promoted by low temperature in the cold season. A 152.4 million China Yuan (CNY) economic loss could be avoided if concentrations of PM_2.5 and PM₁₀ declined to 10 and 20 μg/m³, respectively.

Conclusions

PM_2.5 and PM₁₀ concentrations were positively associated with RD hospitalization. Men and children were more vulnerable to PM. Effective air pollution control measures can reduce hospitalizations significantly and save economic loss substantially.

Coordinated health effects attributable to particulate matter and other pollutants exposures in the North China Plain

Hebei Province, located in the North China Plain (NCP) and encircling Beijing and Tianjin, has been suffering from severe air pollution. The monthly average fine particulate matter (PM_2.5) concentration was up to 276 μg/m³ in Hebei Province, which adversely affects human health. However, few studies evaluated the coordinated health impact of exposure to PM (PM_2.5 and PM₁₀) and other key air pollutants (SO₂, NO₂, CO, and surface ozone (O₃)). In this study, we systematically analyzed the health risks (both mortality and morbidity) due to multiple air pollutants exposures in Hebei Province. The economic loss associated with these health consequences was estimated using the value of statistical life (VSL) and cost of illness (COI) methods. Our results show the health burden and economic loss attributable to multiple ambient air pollutants exposures in Hebei Province is substantial. In 2017, the total premature mortality from multiple air pollutants exposures in Hebei Province was 69,833 (95% CI: 55,549-83,028), which was 2.9 times higher than that of the Pearl River Delta region (PRD). Most of the potential economic loss (79.65%) was attributable to premature mortality from air pollution. The total economic loss due to the health consequences of multiple air pollutants exposures was 175.16 (95% CI: 134.61-224.61) billion Chinese Yuan (CNY), which was 4.92% of Hebei Province's annual gross domestic product (GDP). Thus, the adverse health effects and economic loss caused by exposure to multiple air pollutants should be seriously taken into consideration. To alleviate these damages, Hebei's government ought to establish more stringent measures and regulations to better control air pollution.

Assessment of summertime O3 formation and the O3-NOX-VOC sensitivity in Zhengzhou, China using an observation-based model

Zhengzhou, the provincial capital of Henan province in Central China and a major hub of the country's transportation network, has been suffering from severe summertime ozone (O₃) pollution. Simultaneous field measurements of O₃ and its precursors, including NO_x, CO, HONO, and 106 volatile organic compounds (VOCs), were conducted at an urban site (the municipal environmental monitoring station, MEM) in Zhengzhou in July 2019. The Community Multiscale Air Quality (CMAQ) model, which incorporates the Master Chemical Mechanism (MCMv3.3.1), was modified to work as a 0-D observation-based photochemical box model to assess the sources and sinks of HO_x radicals and O₃, and the OH reactivity (K_OH) and ozone formation potential (OFP) of major VOC groups. In addition, the O₃-NO_x-VOC sensitivity was evaluated using the relative incremental reactivity (RIR) and O₃ formation isopleth techniques. The OH radicals were mainly generated from the propagation reaction of HO₂ + NO (91-95%). The daily average mixing ratios of OH and HO₂ radicals were significantly higher during high O₃ days, reaching as high as 4.8 × 10⁶ and 7.7 × 10⁸ molecules cm^-3, respectively. Photochemical O₃ formation was mostly due to the conversion of NO to NO₂ by HO₂ radicals (52-54%), while the NO₂ + OH reaction was the main contributor to O₃ destruction (70- 76%). Alkenes and aromatics were the main anthropogenic VOC contributors to K_OH and OFP. Contributions of biogenic VOCs became much more important on high O₃ days, correlating with the increase in temperature and solar radiation. RIR analysis showed that the O₃ formation was under the VOC-limited on low O₃ days but was in the transition regime during the O₃ pollution buildup and persisting days. These results are generally consistent with those based on the O₃ formation isopleth. This paper provides important corroborative scientific evidence urgently needed by local governments to formulate O₃ pollution control strategies.

Analysis of the spatio-temporal network of air pollution in the Yangtze River Delta urban agglomeration, China

The complex correlation between regions caused by the externality of air pollution increases the difficulty of its governance. Therefore, analysis of the spatio-temporal network of air pollution (STN-AP) holds great significance for the cross-regional coordinated governance of air pollution. Although the spatio-temporal distribution of air pollution has been analyzed, the structural characteristics of the STN-AP still need to be clarified. The STN-AP in the Yangtze River Delta urban agglomeration (YRDUA) is constructed based on the improved gravity model and is visualized by UCINET with data from 2012 to 2019. Then, its overall-individual-clustering characteristics are analyzed through social network analysis (SNA) method. The results show that the STN-AP in the YRDUA was overall stable, and the correlation level gradually improved. The centrality of every individual city is different in the STN-AP, which reveals the different state of their interactive mechanism. The STN-AP could be subdivided into the receptive block, overflow block, bidirectional block and intermediary block. Shanghai, Suzhou, Hangzhou and Wuxi could be key cities with an all above degree centrality, betweenness centrality and closeness centrality and located in the overflow block of the STN-AP. This showed that these cities had a greater impact on the STN-AP and caused a more pronounced air pollution spillovers. The influencing factors of the spatial correlation of air pollution are further determined through the quadratic assignment procedure (QAP) method. Among all factors, geographical proximity has the strongest impact and deserves to be paid attention in order to prevent the cross-regional overflow of air pollution. Furthermore, several suggestions are proposed to promote coordinated governance of air pollution in the YRDUA.

Does the "Blue Sky Defense War Policy" Paint the Sky Blue?-A Case Study of Beijing-Tianjin-Hebei Region, China

Improving air quality is an urgent task for the Beijing-Tianjin-Hebei (BTH) region in China. In 2018, utilizing 365 days' daily concentration data of six air pollutants (including PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃) at 947 air quality grid monitoring points of 13 cities in the BTH region and controlling the meteorological factors, this paper takes the implementation of the Blue Sky Defense War (BSDW) policy as a quasi-natural experiment to examine the emission reduction effect of the policy in the BTH region by applying the difference-in-difference method. Results show that the policy leads to the significant reduction of the daily average concentration of PM_2.5, PM₁₀, SO₂, O₃ by -1.951 μg/m³, -3.872 μg/m³, -1.902 μg/m³, -7.882 μg/m³ and CO by -0.014 mg/m³, respectively. The results of the robustness test support the aforementioned conclusions. However, this paper finds that the concentration of NO₂ increases significantly (1.865 μg/m³). In winter heating seasons, the concentration of SO₂, CO and O₃ decrease but PM_2.5, PM₁₀ and NO₂ increase significantly. Besides, resource intensive cities, non-key environmental protection cities and cities in the north of the region have great potential for air pollutant emission reduction. Finally, policy suggestions are recommended; these include setting specific goals at the city level, incorporating more cities into the list of key environmental protection cities, refining the concrete indicators of domestic solid fuel, and encouraging and enforcing clean heating diffusion.

Research on the Temporal and Spatial Characteristics of Air Pollutants in Sichuan Basin

Sichuan Basin is one of the most densely populated areas in China and the world. Human activities have great impact on the air quality. In order to understand the characteristics of overall air pollutants in Sichuan Basin in recent years, we analyzed the concentrations of six air pollutants monitored in 22 cities during the period from January 2015 to December 2020. During the study period, the annual average concentrations of CO, NO2, SO2, PM2.5 and PM10 all showed a clear downward trend, while the ozone concentration was slowly increasing. The spatial patterns of CO and SO2 were similar. High-concentration areas were mainly located in the western plateau of Sichuan Basin, while the concentrations of NO2 and particulate matter were more prominent in the urban agglomerations inside the basin. During the study period, changes of the monthly average concentrations for pollutants (except for O3) conformed to the U-shaped pattern, with the highest in winter and the lowest in summer. In the southern cities of the basin, secondary sources had a higher contribution to the generation of fine particulate matter, while in large cities inside the basin, such as Chengdu and Chongqing, air pollution had a strong correlation with automobile exhaust emissions. The heavy pollution incidents observed in the winter of 2017 were mainly caused by the surrounding plateau terrain with typical stagnant weather conditions. This finding was also supported by the backward trajectory analysis, which showed that the air masses arrived in Chengdu were mainly from the western plateau area of the basin. The results of this study will provide a basis for the government to take measures to improve the air quality in Sichuan Basin.

Lifetime exposure of ambient PM2.5 elevates intraocular pressure in young mice

Epidemiological studies suggest that ambient particulate matter exposure may be a new risk factor of glaucoma, but it lacks solid experimental evidence to establish a causal relationship. In this study, young mice (4 weeks old) were exposed concentrated ambient PM2.5 (CAP) for 9 months, which is throughout most of the life span of a mouse under heavy pollution. CAP was introduced using a versatile aerosol concentration enrichment system which mimics natural PM2.5 exposure. CAP exposure caused a gradual elevation of intraocular pressure (IOP) and an increase in aqueous humor outflow resistance. In the conventional outflow tissues that regulates IOP, inducible nitric oxide synthase (iNOS) was up-regulated and 3-nitrotyrosine (3-NT) formation increased. At the cellular level, PM2.5 exposure increased the transendothelial electrical resistance of cells that control IOP (AAP cells). This is accompanied by increased reactive oxygen species (ROS), iNOS and 3-NT levels. Peroxynitrite scavenger MnTMPyP successfully treated the IOP elevation and restored it to normal levels by reducing 3-NT formation in outflow tissues. This study provides the novel evidence that in young mice, lifetime whole-body PM2.5 exposure has a direct toxic effect on intraocular tissues, which imposes a significant risk of IOP elevation and may initiate the development of ocular hypertension and glaucoma. This occurs as a result of protein nitration of conventional aqueous humor outflow tissues.

Industrial agglomeration and air pollution: A new perspective from enterprises in Atmospheric Pollution Transmission Channel Cities (APTCC) of Beijing-Tianjin-Hebei and its surrounding areas, China

Air quality in China has gradually been improving in recent years; however, the Beijing-Tianjin-Hebei (BTH) region continues to be the most polluted area in China, with the worst air quality index. BTH and its surrounding areas experience high agglomeration of heavy-polluting manufacturers that generate electric power, process petroleum and coal, and carry out smelting and pressing of ferrous metals, raw chemical materials, chemical products, and non-metallic mineral products. This study presents evidence of the air pollution impacts of industrial agglomeration using the Ellison-Glaeser index, Herfindahl-Hirschman index, and spatial autocorrelation analysis. This was based on data from 73,353 enterprises in "2+26" atmospheric pollution transmission channel cities in BTH and its surrounding areas (herein referred to as BTH "2+26" cities). The results showed that Beijing, Yangquan, Puyang, Kaifeng, Taiyuan, and Jinan had the highest Ellison-Glaeser index among the BTH "2+26" cities; this represents the highest enterprise agglomeration. Beijing, Langfang, Tianjin, Baoding, and Tangshan also showed a low Herfindahl-Hirschman index of pollutant emissions, which have a relatively high degree of industrial agglomeration in BTH "2+26" cities. There was an inverted U-shaped relationship between enterprise agglomeration and air quality in the BTH "2+26" cities. This means that air quality improved with increased industrial agglomeration up to a certain level; beyond this point, the air quality begins to deteriorate with a decrease in industrial agglomeration.

Influence of urban morphological parameters on the distribution and diffusion of air pollutants: A case study in China

Air pollution has a serious fallout on human health, and the influences of the different urban morphological characteristics on air pollutants cannot be ignored. In this study, the relationship between urban morphology and air quality (wind speed, CO, and PM_2.5) in residential neighborhoods at the meso-microscale was investigated. The changes in the microclimate and pollutant diffusion distribution in the neighborhood under diverse weather conditions were simulated by Computational Fluid Dynamics (CFD). This study identified five key urban morphological parameters (Building Density, Average Building Height, Standard Deviation of Building Height, Mean Building Volume, and Degree of Enclosure) which significantly impacted the diffusion and distribution of pollutants in the neighborhood. The findings of this study suggested that three specific strategies (e.g. volume of a single building should be reduced, DE should be increased) and one comprehensive strategy (the width and height of the single building should be reduced while the number of single buildings should be increased) could be illustrated as an optimized approach of urban planning to relief the air pollution. The result of the combined effects could provide a reference for mitigating air pollution in sustainable urban environments.

How far climatic parameters associated with air quality induced risk state (AQiRS) during COVID-19 persuaded lockdown in India

Global temperature rises in response to accumulating greenhouse gases is a well-debated issue in the present time. Historical records show that greenhouse gases positively influence temperature. Lockdown incident has brought an opportunity to justify the relation between greenhouse gas centric air pollutants and climatic variables considering a concise period. The present work has intended to explore the trend of air quality parameters, and air quality induced risk state since pre to during the lockdown period in reference to India and justifies the influence of pollutant parameters on climatic variables. Results showed that after implementation of lockdown, about 70% area experienced air quality improvement during the lockdown. The hazardous area was reduced from 7.52% to 5.17%. The spatial association between air quality components and climatic variables were not found very strong in all the cases. Still, statistically, a significant relation was observed in the case of surface pressure and moisture. From this, it can be stated that pollutant components can control the climatic components. This study recommends that pollution source management could be a partially good step for bringing climatic resilience of a region.

Aircraft-based observation of gaseous pollutants in the lower troposphere over the Beijing-Tianjin-Hebei region

To investigate the spatial and vertical distribution of atmospheric pollutants (SO₂, NO_x, CO and O₃), aircraft-based measurements (model: Yun-12, 12 flights, 27 h total flight time) were conducted from near the surface up to 2400 m over the Beijing-Tianjin-Hebei (BTH) region between June 17th and July 22nd 2016. The results showed that high concentrations of primary gaseous pollutants (SO₂, NO_x, CO) were generally present in Beijing, Tianjin, Langfang and Tangshan areas, while high values of O₃ frequently appeared in areas far from the city. The flights at noon and dusk measured higher O₃ concentrations at 600 m and lower O₃ concentrations at higher altitudes, implying a strong influence by photochemical production. Back trajectory analysis suggested that the high levels of gaseous pollutants, especially at 600 m, were associated with pollution sources transported from the southerly direction during the observation period. The first simultaneous vertical distribution measurements using aircraft and tethered balloon were conducted in Gaocun (a rural site between Beijing and Tianjin) on June 17th. The results indicated that an inversion layer at the top of the planetary boundary layer (PBL) significantly suppressed vertical exchange through the PBL and resulted in a "two-layer" vertical distribution of pollutants above and below the PBL. Additionally, a residual high O₃ layer (79.9 ± 2.5 ppb, 500-1000 m) was observed above the PBL, and it contributed to the surface peak O₃ level at noon through downward transport along with the opening up of the PBL. These results indicate that coupled effects of horizontal and vertical transport should be investigated in future studies to improve the chemical transport models used to study the vertical distribution and regional transport over the BTH region.

Spatial Characteristics of PM2.5 Pollution among Cities and Policy Implication in the Northern Part of the North China Plain

In the recent decade, the North China Plain (NCP) has been among the region’s most heavily polluted by PM2.5 in China. For the nonattainment cities in the NCP, joint pollution control with related cities is highly needed in addition to the emission controls in their own cities. However, as the basis of decision-making, the spatial characteristics of PM2.5 among these cities are still insufficiently revealed. In this work, the spatial characteristics among all nonattainment cities in the northern part of the North China Plain (NNCP) region were revealed based on data mining technologies including clustering, coefficient of divergence (COD), network correlation model, and terrain and meteorology analysis. The results indicate that PM2.5 pollution of cities with a distance of less than 180 km exhibits homogeneity in the NCP region. Especially, the sub-region, composed of Xinxiang, Hebi, Kaifeng, Zhengzhou, and Jiaozuo, was strongly homogeneous and a strong correlation exists among them. Compared with spring and summer, much stronger correlations of PM2.5 between cities were found in autumn and winter, indicating a strong need for joint prevention and control during these periods. All nonattainment cities in this region were divided into city-clusters, depending on the seasons and pollution levels to further helping to reduce their PM2.5 concentrations effectively. Air stagnation index (ASI) analysis indicates that the strong correlations between cities in autumn were more attributed to the transport impacts than those in winter, even though there were higher PM2.5 concentrations in winter. These results provided an insight into joint prevention and control of pollution in the NCP region.

Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China

Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution and has been actively involved in air pollution control in recent years to achieve sustainable development. Based on the historical data of 16 cities in this region from January 2015 to November 2019 on six major air pollutants, this paper has first conducted evaluation on the monthly air quality of these cities within the research period by using Principal Component Analysis and the Technique for Order Preference by Similarity to an Ideal Solution. Based on that, this paper has adopted the Long Short-Term Memory neural network model in deep learning to forecast the monthly air quality of various cities from December 2019 to November 2020. The aims of this paper are to enrich existing literature on air pollution control, and provide a novel scientific tool for design and formulation of air pollution control policies by innovatively integrating commonly used evaluation models and deep learning forecast methods. According to the research results, in terms of historical evaluation, the air quality of cities in the Chengdu-Chongqing region was generally moving in the same trend in the research period, with distinct characteristics of cyclicity and convergence. Year- on-year speaking, the effectiveness of air pollution control in various cities has shown a visible improvement trend. For example, Ya’an’s lowest air quality evaluation score has improved from 0.3494 in 2015 to 0.4504 in 2019; Zigong’s lowest air quality score has also risen from 0.4160 in 2015 to 0.6429 in 2019. Based on the above historical evaluation and deep learning forecast results, this paper has proposed relevant policy recommendations for air pollution control in the Chengdu-Chongqing region.

Has air pollution emission level in the Beijing-Tianjin-Hebei region peaked? A panel data analysis

The Beijing-Tianjin-Hebei (BTH) region is one of the important economic centers of China, but it suffers from severe air pollution. Based on the panel pollution-related data of 2013-2017, this research adopted a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) equation to fit the relationship between pollution emission level and its related socio-economic indicators. The pollution emission level of the BTH region was fitted and projected by using the entropy evaluation method to measure the emission levels, the partial least squares algorithm to estimate the STIRPAT equation parameters, and the hybrid trend extrapolation model to forecast the future development of the above socioeconomic indicators. Empirical analysis showed that the fitting curve to air pollution emission level reached the peak in 2015 and then decreased with a fluctuating and slow process. The air pollution emissions in 2025 will decrease to the level of 2007. With regard to the impacts on the change of the air emission pollution level, industrial waste gas emissions play a decisive role. The influence of soot (dust) emissions is considerably smaller but still larger than that of SO₂ emissions. Besides, the slowing down of the economic development in the future will contribute to air quality improvement. However, the rapid growth of population in Hebei and Tianjin would hinder such improvement. Empirical analysis also implied that governments in this region should specially monitor the operation of building material industries to ensure the steady improvement of air quality.

Describing the trend of ammonia, particulate matter and nitrogen oxides: The role of livestock activities in northern Italy during Covid-19 quarantine

Nitrogen oxides (NOx), sulphur oxides (SOx) and ammonia (NH3) are among the main contributors to the formation of secondary particulate matter (PM2.5), which represent a severe risk to human health. Even if important improvements have been achieved worldwide, traffic, industrial activities, and the energy sector are mostly responsible for NOx and SOx release; instead, the agricultural sector is mainly responsible for NH3 emissions. Due to the emergency of coronavirus disease, in Italy schools and universities have been locked down from late February 2020, followed in March by almost all production and industrial activities as well as road transport, except for the agricultural ones. This study aims to analyze NH3, PM2.5 and NOx emissions in principal livestock provinces in the Lombardy region (Brescia, Cremona, Lodi, and Mantua) to evaluate if and how air emissions have changed during this quarantine period respect to 2016-2019. For each province, meteorological and air quality data were collected from the database of the Regional Agency for the Protection of the Environment, considering both data stations located in the city and the countryside. In the 2020 selected period, PM2.5 reduction was higher compared to the previous years, especially in February and March. Respect to February, PM2.5 released in March in the city stations reduced by 19%-32% in 2016-2019 and by 21%-41% in 2020. Similarly, NOx data of 2020 were lower than in the 2016-2019 period (reduction in March respect to February of 22-42% for 2016-2019 and of 43-62% for 2020); in particular, this can be observed in city stations, because of the current reduction in anthropogenic emissions related to traffic and industrial activities. A different trend with no reductions was observed for NH3 emissions, as agricultural activities have not stopped during the lockdown. Air quality is affected by many variables, for which making conclusions requires a holistic perspective. Therefore, all sectors must play a role to contribute to the reduction of harmful pollutants.

Identification of Long-Range Transport Pathways and Potential Source Regions of PM2.5 and PM10 at Akedala Station, Central Asia

Cluster analyses, potential source contribution function (PSCF) and concentration-weight trajectory (CWT) were used to identify the main transport pathways and potential source regions with hourly PM2.5 and PM10 concentrations in different seasons from January 2017 to December 2019 at Akedala Station, located in northwest China (Central Asia). The annual mean concentrations of PM2.5 and PM10 were 11.63 ± 9.31 and 19.99 ± 14.39 µg/m3, respectively. The air pollution was most polluted in winter, and the dominant part of PM10 (between 54 to 76%) constituted PM2.5 aerosols in Akedala. Particulate pollution in Akedala can be traced back to eastern Kazakhstan, northern Xinjiang, and western Mongolia. The cluster analyses showed that the Akedala atmosphere was mainly affected by air masses transported from the northwest. The PM2.5 and PM10 mainly came with air masses from the central and eastern regions of Kazakhstan, which are characterized by highly industrialized and semi-arid desert areas. In addition, the analyses of the pressure profile of back-trajectories showed that air mass distribution were mainly distributed above 840 hPa. This indicates that PM2.5 and PM10 concentrations were strongly affected by high altitude air masses. According to the results of the PSCF and CWT methods, the main potential source areas of PM2.5 were very similar to those of PM10. In winter and autumn, the main potential source areas with high weighted PSCF values were located in the eastern regions of Kazakhstan, northern Xinjiang, and western Mongolia. These areas contributed the highest PM2.5 concentrations from 25 to 40 µg/m3 and PM10 concentrations from 30 to 60 µg/m3 in these seasons. In spring and summer, the potential source areas with the high weighted PSCF values were distributed in eastern Kazakhstan, northern Xinjiang, the border between northeast Kazakhstan, and southern Russia. These areas contributed the highest PM2.5 concentrations from 10 to 20 µg/m3 and PM10 concentrations from 20 to 60 µg/m3 in these seasons.

The Impact of the Control Measures during the COVID-19 Outbreak on Air Pollution in China

The outbreak of the COVID-19 virus in Wuhan, China, in January 2020 just before the Spring Festival and subsequent country-wide measures to contain the virus, effectively resulted in the lock-down of the country. Most industries and businesses were closed, traffic was largely reduced, and people were restrained to their homes. This resulted in the reduction of emissions of trace gases and aerosols, the concentrations of which were strongly reduced in many cities around the country. Satellite imagery from the TROPOspheric Monitoring Instrument (TROPOMI) showed an enormous reduction of tropospheric NO2 concentrations, but aerosol optical depth (AOD), as a measure of the amount of aerosols, was less affected, likely due to the different formation mechanisms and the influence of meteorological factors. In this study, satellite data and ground-based observations were used together to estimate the separate effects of the Spring Festival and the COVID-19 containment measures on atmospheric composition in the winter of 2020. To achieve this, data were analyzed for a period from 30 days before to 60 days after the Spring Festivals in 2017–2020. This extended period of time, including similar periods in previous years, were selected to account for both the decreasing concentrations in response to air pollution control measures, and meteorological effects on concentrations of trace gases and aerosols. Satellite data from TROPOMI provided the spatial distributions over mainland China of the tropospheric vertical column density (VCD) of NO2, and VCD of SO2 and CO. The MODerate resolution Imaging Spectroradiometer (MODIS) provided the aerosol optical depth (AOD). The comparison of the satellite data for different periods showed a large reduction of, e.g., NO2 tropospheric VCDs due to the Spring Festival of up to 80% in some regions, and an additional reduction due to the COVID-19 containment measures of up to 70% in highly populated areas with intensive anthropogenic activities. In other areas, both effects are very small. Ground-based in situ observations from 26 provincial capitals provided concentrations of NO2, SO2, CO, O3, PM2.5, and PM10. The analysis of these data was focused on the situation in Wuhan, based on daily averaged concentrations. The NO2 concentrations started to decrease a few days before the Spring Festival and increased after about two weeks, except in 2020 when they continued to be low. SO2 concentrations behaved in a similar way, whereas CO, PM2.5, and PM10 also decreased during the Spring Festival but did not trace NO2 concentrations as SO2 did. As could be expected from atmospheric chemistry considerations, O3 concentrations increased. The analysis of the effects of the Spring Festival and the COVID-19 containment measures was complicated due to meteorological influences. Uncertainties contributing to the estimates of the different effects on the trace gas concentrations are discussed. The situation in Wuhan is compared with that in 26 provincial capitals based on 30-day averages for four years, showing different effects across China.

Evaluation of the Level of Sustainable Development of Provinces in China from 2012 to 2018: A Study Based on the Improved Entropy Coefficient-TOPSIS Method

Based on the definition and implication of sustainable development, this paper first constructed an evaluation indicator system for the sustainable development level of provinces in China, and performed a scientific evaluation on the sustainable development level based on official statistics from 2012 to 2018 by using the improved Entropy Coefficient-TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. The evaluation results showed that the eastern region of China has the highest level of sustainable development, with its two municipalities directly under the central government, Beijing and Shanghai, achieving the full score of 1.0000 in all evaluations, both ranking first among all the provinces. There were significant differences in the level of sustainable development across provinces in the central region, which were comparatively weaker in terms of environmental sustainability and science and technology sustainability, with four provinces’ evaluation scores below 0.5000. The provinces of the western region had comparatively lower levels of sustainable development, with six of the provinces ranking among the bottom ten in the overall sustainability score. In the northeast region, Liaoning had the highest overall sustainable development level, ranking ninth in the country, with an evaluation score of 0.7726; however, there were large differences across the region, with the other two provinces ranking 19th and 21th, respectively, in the overall sustainability score. Based on the research findings, this paper has provided relevant policy recommendations for China to further improve the sustainable development level of various provinces in the future.

Spatiotemporal Characteristics of Air Quality across Weifang from 2014-2018

Air pollution has become a severe threat and challenge in China. Focusing on air quality in a heavily polluted city (Weifang Cty), this study aims to investigate spatial and temporal distribution characteristics of air pollution and identify the influence of weather factors on primary pollutants in Weifang over a long period from 2014-2018. The results indicate the annual Air quality Index (AQI) in Weifang has decreased since 2014 but is still far from the standard for excellent air quality. The primary pollutants are O3 (Ozone), PM10 (Particles with aerodynamic diameter ≤10 µm), and PM2.5 (Particles with aerodynamic diameter ≤10 µm); the annual concentrations of PM10 and PM2.5 show a significant reduction but that of O3 is basically unchanged. Seasonally, PM10 and PM2.5 show a U-shaped pattern, while O3 exhibits inverted U-shaped variations, and different pollutants also present different characteristics daily. Spatially, O3 exhibits a high level in the central region and a low level in the rural areas, while PM10 and PM2.5 are high in the northwest and low in the southeast. Additionally, the concentration of pollutants is greatly affected by meteorological factors, with PM2.5 being negatively correlated with temperature and wind speed, while O3 is positively correlated with the temperature. This research investigated the spatiotemporal characteristics of the air pollution and provided important policy advice based on the findings, which can be used to mitigate air pollution.