Spatio-temporal variations of PM2.5 emission in China from 2005 to 2014

Long-Term Exposure to Fine Particulate Matter and Fasting Blood Glucose and Diabetes in 20 Million Chinese Women of Reproductive Age

OBJECTIVE

Evidence of the associations between fine particulate matter (PM2.5) and diabetes risk from women of reproductive age, in whom diabetes may have adverse long-term health effects for both themselves and future generations, remains scarce. We therefore examined the associations of long-term PM2.5 exposure with fasting blood glucose (FBG) level and diabetes risk in women of reproductive age in China.

RESEARCH DESIGN AND METHODS

This study included 20,076,032 women age 20-49 years participating in the National Free Preconception Health Examination Project in China between 2010 and 2015. PM2.5 was estimated using a satellite-based model. Multivariate linear and logistic regression models were used to examine the associations of PM2.5 exposure with FBG level and diabetes risk, respectively. Diabetes burden attributable to PM2.5 was estimated using attributable fraction (AF) and attributable number.

RESULTS

PM2.5 showed monotonic relationships with elevated FBG level and diabetes risk. Each interquartile range (27 μg/m3) increase in 3-year average PM2.5 concentration was associated with a 0.078 mmol/L (95% CI 0.077, 0.079) increase in FBG and 18% (95% CI 16%, 19%) higher risk of diabetes. The AF attributed to PM2.5 exposure exceeding 5 μg/m3 was 29.0% (95% CI 27.5%, 30.5%), corresponding to an additional 78.6 thousand (95% CI 74.5, 82.6) diabetes cases. Subgroup analyses showed more pronounced diabetes risks in those who were overweight or obese, age >35 years, less educated, of minority ethnicity, registered as a rural household, and residing in western China.

CONCLUSIONS

We found long-term PM2.5 exposure was associated with higher diabetes risk in women of reproductive age in China.

PM2.5 concentration prediction based on EEMD-ALSTM

The concentration prediction of PM2.5 plays a vital role in controlling the air and improving the environment. This paper proposes a prediction model (namely EEMD-ALSTM) based on Ensemble Empirical Mode Decomposition (EEMD), Attention Mechanism and Long Short-Term Memory network (LSTM). Through the combination of decomposition and LSTM, attention mechanism is introduced to realize the prediction of PM2.5 concentration. The advantage of EEMD-ALSTM model is that it decomposes and combines the original data using the method of ensemble empirical mode decomposition, reduces the high nonlinearity of the original data, and Specially reintroduction the attention mechanism, which enhances the extraction and retention of data features by the model. Through experimental comparison, it was found that the EEMD-ALSTM model reduced its MAE and RMSE by about 15% while maintaining the same R2 correlation coefficient, and the stability of the model in the prediction process was also improved significantly.

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.

Oxidative stress indices induced by industrial and urban PM2.5-bound metals in A549 cells

The detrimental effects of atmospheric fine particulate matter (PM_2.5) on human health are of major global concern. PM_2.5-bound metals are toxic compounds that contribute to cellular damage. To investigate the toxic effects of water-soluble metals on human lung epithelial cells and their bioaccessibility to lung fluid, PM_2.5 samples were collected from both urban and industrial areas in the metropolitan city of Tabriz, Iran. Oxidative stress indices, including proline content, total antioxidant capacity (TAC), cytotoxicity, and DNA damage levels of water-soluble components of PM_2.5, were evaluated. Furthermore, an in vitro test was conducted to assess the bioaccessibility of various PM_2.5-bound metals to the respiratory system using simulated lung fluid. PM_2.5 average concentrations in urban and industrial areas were 83.11 and 97.71 μg/m³, respectively. The cytotoxicity effects of PM_2.5 water-soluble constituents from urban areas were significantly higher than in industrial areas and the IC₅₀ was found to be 96.76 ± 3.34 and 201.31 ± 5.96 μg/mL for urban and industrial PM_2.5 samples, respectively. In addition, higher PM_2.5 concentrations increased the proline content in a concentration-dependent manner in A549 cells, which plays a protective role against oxidative stress and prevents PM_2.5-induced DNA damage. Also, the partial least squares regression revealed that Be, Cd, Co, Ni, and Cr, were significantly correlated with DNA damage and proline accumulation, which caused cell damage through oxidative stress. The results of this study showed that PM_2.5-bound metals in highly polluted metropolitan city caused substantial changes in the cellular proline content, DNA damage levels and cytotoxicity in human lung A549 cells.

Spatiotemporal characteristics and socioeconomic factors of PM2.5 heterogeneity in mainland China during the COVID-19 epidemic

Spatiotemporal variation of PM_2.5 in 2018 and 2020 were compared to analyze the impacts of COVID-19, the spatial heterogeneity of PM_2.5, and meteorological and socioeconomic impacts of PM_2.5 concentrations heterogeneity in China in 2020 were investigated. The results showed that the annual average PM_2.5 concentration in 2020 was 32.73 μg/m³ existing a U-shaped variation pattern, which has decreased by 6.38 μg/m³ compared to 2018. A consistent temporal pattern was found in 2018 and 2020 with significant high values in winter and low in summer. PM_2.5 declined dramatically in eastern and central China, where are densely populated and economically developed areas during the COVID-19 epidemic compared with previous years, indicating that the significantly decline of social activities had an important effect on the reduction of PM_2.5 concentrations. The lowest PM_2.5 was found in August because that precipitation had a certain dilution effect on pollutants. January was the most polluted due to centralized coal burning for heating in North China. Overall, the PM_2.5 concentrations in China were spatially agglomerated. The highly polluted contiguous zones were mainly located in northwest China and the central plains city group, while the coastal area and Inner Mongolia were areas with good air quality. Negative correlations were found between natural factors (temperature, precipitation, wind speed and relative humidity) and PM_2.5 concentrations, with precipitation has the greatest impact on PM_2.5, which are beneficial for reducing PM_2.5 concentrations. Among the socio-economic factors, proportion of the secondary industry, number of taxis, per capita GDP, population, and industrial nitrogen oxide emissions have positive correlation effects on PM_2.5, while the overall social electricity consumption, industrial sulfur dioxide emissions, green coverage in built-up areas, and total gas and liquefied gas supply have negative correlation effects on the PM_2.5.

County level study of the interaction effect of PM2.5 and climate sustainability on mortality in China

Introduction

PM2.5 and climate change are two major public health concerns, with majority of the research on their interaction focused on the synergistic effect, particularly for extreme events such as hot or cold temperatures. The climate sustainability index (CLS) was introduced to comprehensively explore the impact of climate change and the interactive effect on human health with air pollution.

Methods

In this study, a county-level panel data in China was collected and used. The generalized additive model (GAM) and geographically and temporally weighted regression (GTWR) was used to explore the interactive and spatial effect on mortality between CLS and PM2.5.

Results and discussions

Individually, when CLS is higher than 150 or lower than 50, the mortality is higher. Moreover, when PM2.5 is more than 35 μg/m3, the influence on mortality is significantly increased as PM2.5 concentration rises; when PM2.5 is above 70 μg/m3, the trend is sharp. A nonlinear antagonistic effect between CLS and PM2.5 was found in this study, proving that the combined adverse health effects of climate change and air pollution, especially when CLS was lower (below 100) and PM2.5 was higher (above 35 μg/m3), the antagonistic effect was much stronger. From a spatial perspective, the impact of CLS and PM2.5 on mortality varies in different geographical regions. A negative and positive influence of CLS and PM2.5 was found in east China, especially in the northeastern and northern regions, -which were heavily polluted. This study illustrated that climate sustainability, at certain level, could mitigate the adverse health influence of air pollution, and provided a new perspective on health risk mitigation from pollution reduction and climate adaptation.

Spatial and temporal variations of PM2.5 concentrations in Chinese cities during 2015-2019

The study analyzed the current status and changing trends of PM_2.5 pollution, and used Kriging spatial interpolation, spatial autocorrelation analysis, and scan statistics to explore the spatiotemporal characteristics and identify hotspots. The results showed that PM_2.5 pollution during 2015-2019 displayed a downward trend year by year, with a pronounced seasonal difference of higher concentrations in winter and lower concentrations in summer. By 2019, there were still 110 cities (n = 194) failed to meet China's annual grade II air quality standard (35 μg/m³). The spatial distribution of PM_2.5 was characterized by marked heterogeneity, with a significant spatial dependence and clustering characteristics. The pollution hotspots of PM_2.5 were mainly concentrated in eastern and central China, especially in the Beijing-Tianjin-Hebei region and its surrounding area. The results of this study will assist Chinese authorities in developing strategies for preventing and controlling air pollution, especially in hotspot regions and during peak periods.

Estimates of PM2.5 concentrations spatiotemporal evolution across China considering aerosol components in the context of the Reform and Opening-up

With astonishing and rapid development in China since the Reform and Opening-up in 1978, serious air pollution has become a great challenge. A better understanding of the response of PM_2.5 pollution to socioeconomic development after the Reform and Opening-up policy is benefit for pollution control. However, heterogeneous influences of biophysical and socioeconomic activities on PM_2.5 pollution pose great challenges in statistical simulation of PM_2.5. Few statistical model regards aerosol species as the explanatory variables for heterogeneous formation mechanism to retrieve PM_2.5 concentration. In this research, monthly PM_2.5 concentration in China during 1980-2020 was reconstructed by a novel statistical strategy considering aerosol components (AC-RF). Three cross-validation (CV) methods, sample-based CV, spatial-based CV and temporal-based CV results indicated satisfactory performance of AC-RF model with correlation coefficient (R) of 0.92, 0.90, 0.86, respectively. A three-stage concluded on PM_2.5 concentration annual variation in China was drawn as followed: Before 2000, PM_2.5 level in China represented smooth evolution and mainly influenced by natural events with polluted region locating in Xinjiang province, North China and Central China. Since 2000, PM_2.5 concentration increased to high level in the context of rapid socioeconomic development. Severe air pollution covered Jing-Jin-Ji agglomeration, Central China and Sichuan Basin. During 2012-2020, PM_2.5 declined and polluted region shrank, which was benefited by the strictest-ever air pollution control measures. Based on aerosol components analysis, sulfate aerosol exhibited the most significant increase trend in recent 40 years and black aerosol variation is the most closely related to PM_2.5 pollution. In conclusion, unsustainable development is the culprit for air quality deterioration. Strict and continuous air pollution control strategies are effective for air quality improvement.

Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities

Scientific study of the temporal and spatial distribution characteristics of haze is important for the governance of haze pollution and the formulation of environmental policies. This study used panel data of the concentrations of particulate matter sized < 2.5 μm (PM2.5) in 340 major cities from 1999 to 2016 to calculate the spatial distribution correlation by the spatial analysis method and test the temporal convergence of the urban PM2.5 concentration distribution using an econometric model. It found that the spatial autocorrelation of PM2.5 seemed positive, and this trend increased over time. The yearly concentrations of PM2.5 were converged, and the temporal convergence fluctuated under the influence of specific historical events and economic backgrounds. The spatial agglomeration effect of PM2.5 concentrations in adjacent areas weakened the temporal convergence of PM2.5 concentrations. This paper introduced policy implications for haze prevention and control.

Assessment of the temporal variability and health risk of atmospheric particle-phase polycyclic aromatic hydrocarbons in a northeastern city in China

In this study, we examined the sources and temporal variability of 16 polycyclic aromatic hydrocarbons (PAHs) found in fine particulate matter (PM_2.5) in a typical industrial city in northern China. We also evaluated the incremental lifetime cancer risk (ILCR) from the inhalation of these PAHs. Atmospheric PM_2.5 samples were collected for 7 consecutive days each month from 2014 to 2019, and the 16 PAHs were measured using multiplex gas chromatography-tandem mass spectrometry. The carcinogenic risk of PAH exposure was assessed using the inhalation unit risk (IUR) and cancer slope factor (CSF) methods. The annual average concentrations of PM_2.5 for each year from 2014 to 2019 were 102.87±55.25, 86.92±60.43, 69.17±37.74, 58.20±59.15, 56.01±34.52, and 52.54±58.15 µg m^-3, and the annual average ΣPAH concentrations were 56.03±81.09, 47.99±79.30, 40.41±57.31, 33.57±51.79, 43.23±74.80, and 25.20±50.91 ng m^-3, respectively. Source identification, using diagnostic ratio analysis, indicated that the major PAH sources were coal/biomass combustion, fuel combustion, and traffic emissions. A health risk assessment showed that the ILCR from PAH inhalation decreased throughout the study period and varied with age. The IUR and CSF methods both showed that the adult ILCR exceeded 1.0×10^-6. These findings demonstrate the importance of addressing the carcinogenic risk of PM_2.5-bound PAHs, particularly in adults.

A new hybrid prediction model of PM2.5 concentration based on secondary decomposition and optimized extreme learning machine

As air pollution worsens, the prediction of PM2.5 concentration becomes increasingly important for public health. This paper proposes a new hybrid prediction model of PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), amplitude-aware permutation entropy (AAPE), variational mode decomposition improved by marine predators algorithm (MPA-VMD), and extreme learning machine optimized by chimp optimization algorithm (ChOA-ELM), named CEEMDAN-AAPE-MPA-VMD-ChOA-ELM. Firstly, CEEMDAN is used to decompose the original data, and AAPE is used to quantify the complexity of all IMF components. Secondly, MPA-VMD is used to decompose the IMF component with the maximum AAPE. Lastly, ChOA-ELM is used to predict all IMF components, and all prediction results are reconstructed to obtain the final prediction results. The proposed model combines the advantages of secondary decomposition technique, feature analysis, and optimization algorithm, which can predict PM2.5 concentration accurately. PM2.5 concentrations at hourly intervals collected from March 1, 2021, to March 31, 2021, in Shanghai and Shenyang, China, are used for experimental study and DM test. The experimental results in Shanghai show that the RMSE, MAE, MAPE, and R2 of the proposed model are 1.0676, 0.7685, 0.0181, and 0.9980 respectively, which is better than all comparison models at 90% confidence level. In Shenyang, the RMSE, MAE, MAPE, and R2 of the proposed model are 1.4399, 1.1258, 0.0389, and 0.9976, respectively, which is better than all comparison models at 95% confidence level.

Spatiotemporal variations of PM2.5 pollution and its dynamic relationships with meteorological conditions in Beijing-Tianjin-Hebei region

Identifying the effects of meteorological conditions on PM_2.5 pollution is of great significance to explore methods to reduce atmospheric pollution. This study attempts to analyze the spatiotemporal variations of PM_2.5 pollution and its dynamic nexus with meteorological factors in the Beijing-Tianjin-Hebei (BTH) region from 2015 to 2020 using standard deviation ellipse (SDE) and panel vector autoregressive (PVAR) model. The results indicate that: (1) In 2015-2020, PM_2.5 pollution decreased significantly, indicating air pollution control policies in China have taken effect; Also, it showed a cumulative effect, or there was the path dependence of air pollution. (2) PM_2.5 pollution presented a distribution pattern from northeast to southwest, while the directionality of air pollution has weakened. Based on SDE, PM_2.5 pollution in Cangzhou can reflect the average level in the BTH; (3) Meteorological conditions exhibited a lagged and sustained effect on PM_2.5 pollution. Specifically, the effects of meteorological factors on PM_2.5 presented disequilibrium over time. In the long run, precipitation and temperature mainly showed negative impacts on PM_2.5 pollution, while wind speed, relative humidity and sunshine duration aggravated PM_2.5 pollution in the BTH. This study contributes to extending the study on the spatiotemporal evolution of PM_2.5 pollution and its links with meteorological conditions.

Fuzzy-Based Ecological Vulnerability Assessment Driven by Human Impacts in China

Human activities have a significant impact on global ecosystems. Assessing and quantifying ecological vulnerability is a fundamental challenge in the study of the ecosystem’s capacity to respond to anthropogenic disturbances. However, little research has been conducted on EVA’s existing fuzzy uncertainties. In this paper, an ecological vulnerability assessment (EVA) framework that integrated the Exposure-Sensitivity-Adaptive Capacity (ESC) framework, fuzzy method, and multiple-criteria decision analysis (MCDA), and took into account human impacts, was developed to address the uncertainties in the assessment process. For the first time, we conducted a provincial-scale case study in China to illustrate our proposed methodology. Our findings imply that China’s ecological vulnerability is spatially heterogeneous due to regional differences in exposure, sensitivity, and adaptive capacity indices. The results of our ecological vulnerability assessment and cause analysis can provide guidance for further decision-making and facilitate the protection of ecological quality over the medium to long term. The developed EVA framework can also be duplicated at multiple spatial and temporal dimensions utilizing context-specific datasets to assist environmental managers in making informed decisions.

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

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

Modeling the Determinants of PM2.5 in China Considering the Localized Spatiotemporal Effects: A Multiscale Geographically Weighted Regression Method

Many studies have identified the influences of PM2.5. However, very little research has addressed the spatiotemporal dependence and heterogeneity in the relationships between impact factors and PM2.5. This study firstly utilizes spatial statistics and time series analysis to investigate the spatial and temporal dependence of PM2.5 at the city level in China using a three-year (2015–2017) dataset. Then, a new local regression model, multiscale geographically weighted regression (MGWR), is introduced, based on which we measure the influence of PM2.5. A spatiotemporal lag is constructed and included in MGWR to account for spatiotemporal dependence and spatial heterogeneity simultaneously. Results of MGWR are comprehensively compared with those of ordinary least square (OLS) and geographically weighted regression (GWR). Experimental results show that PM2.5 is autocorrelated in both space and time. Compared with existing approaches, MGWR with a spatiotemporal lag (MGWRL) achieves a higher goodness-of-fit and a more significant effect on eliminating residual spatial autocorrelation. Parameter estimates from MGWR demonstrate significant spatial heterogeneity, which traditional global models fail to detect. Results also indicate the use of MGWR for generating local spatiotemporal dependence evaluations which are conditioned on various covariates rather than being simple descriptions of a pattern. This study offers a more accurate method to model geographic events.

Influential Nodes Identification in the Air Pollution Spatial Correlation Weighted Networks and Collaborative Governance: Taking China's Three Urban Agglomerations as Examples

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

Exploring the Spatiotemporal Evolution and Socioeconomic Determinants of PM2.5 Distribution and Its Hierarchical Management Policies in 366 Chinese Cities

From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the 3 Year Action Plan to Fight Air Pollution (2018–2020), the pollution status of PM2.5, a typical air pollutant, has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in the Chinese urban atmospheric environment from 2018 to 2020 can be summarized to help conclude and evaluate the phased results of the battle against air pollution, and further, contemplate the governance measures during the period of the 14th Five-Year Plan (2021–2025). Based on PM2.5 data from 2018 to 2020 and taking 366 cities across China as research objects, this study found that PM2.5 pollution has improved year by year from 2018 to 2020, and that the heavily polluted areas were southwest Xinjiang and North China. The number of cities with a PM2.5 concentration in the range of 25–35 μg/m³ increased from 34 in 2018 to 86 in 2019 and 99 in 2020. Moreover, the spatial variation of the PM2.5 gravity center was not significant. Concretely, PM2.5 pollution in 2018 was more serious in the first and fourth quarters, and the shift of the pollution's gravity center from the first quarter to the fourth quarter was small. Global autocorrelation indicated that the space was positively correlated and had strong spatial aggregation. Local Moran's I and Local Geti's G were applied to identify hotspots with a high degree of aggregation. Integrating national population density, hotspots were classified into four areas: the Beijing–Tianjin–Hebei region, the Fenwei Plain, the Yangtze River Delta, and the surrounding areas were selected as the key hotspots for further geographic weighted regression analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: (1) the proportion of secondary industry in the GDP, (2) the ownership of civilian vehicles, (3) the annual grain planting area, (4) the annual average population, (5) the urban construction land area, (6) the green space area, and (7) the per capita GDP. Finally, combined with the spatiotemporal distribution of PM2.5, specific suggestions were provided for the classified key hotspots (Areas A, B, and C), to provide preliminary ideas and countermeasures for PM2.5 control in deep-water areas in the 14th Five-Year Plan.

Spatial and dynamic effects of air pollution on under-five children's lower respiratory infections: an evidence from China 2006 to 2017

Air pollution has been a deeply concerned issue posing an immediate and profound threat to human's lower respiratory health in China. The health of children under 5 years old, regarded as a key index of public health progress in a country, is closely related to the long-term human capital development. Hence, it is vital to investigate the potential association between air pollution and children's lower respiratory health outcomes and to explore related policy implications regarding the public health and the pollution regulation. As air pollutants diffuse across adjacent regions rather easily, considering the spatial spillover effect is meaningful in course of acquiring the aforementioned association. Based on the proposed province-level panel dataset of China during 2006-2017, this study constructs a dynamic spatial panel Durbin model to investigate the impact of air pollution on under-five children's lower respiratory infections. As a result, (1) both air pollution and children's respiratory health have obvious spatial spillover effects, and the latter has an outstanding characteristic of path dependence in time. (2) In the short term, air pollution presents significant negative impact on children's respiratory health, while in the long run, the impact decreases dramatically. (3) Regional comparison indicates that children in the western China are the most susceptible to air pollution followed by children in the central and eastern regions. (4) Other control variables have significant and varying impacts both in the short and long term. Particularly, this paper proves the existence of "siphon effect" in children healthcare system in China. From a broader and more comprehensive perspective, this study provides effective and constructive basis for policy making, in favor of improving children's health under air pollution and promoting sustainable development in China.

Responses of surface O3 and PM2.5 trends to changes of anthropogenic emissions in summer over Beijing during 2014-2019: A study based on multiple linear regression and WRF-Chem

Owing to the implementation of air pollution control actions, anthropogenic emissions in Beijing have changed in recent years. Understanding the impact of changes in anthropogenic emissions on O₃ and PM_2.5 trends is helpful for developing air quality management strategies. Herein, we investigated the variations of air pollutants in summer over Beijing using long-term data sets from 2014 to 2019, and explored the responses of O₃ and PM_2.5 trends to changes in anthropogenic emissions based on multiple linear regression (MLR) analysis and WRF-Chem model. The results indicated a significant decrease in PM_2.5, but a near constant level of O₃ during 2014-2019. The decrease rate of PM_2.5, which was lower than that of SO₂, might be due to the effect of NO₂ on atmospheric PM_2.5. Both the slightly increasing correlations between PM_2.5 and NO₂ and the WRF-Chem model simulations implied that atmospheric PM_2.5 in Beijing is trending to be more sensitive to NO_x than SO₂. The emissions of NO_x and VOCs from industry and transportation were found to make great contribution to O₃ production in Beijing. Due to the titration of NO_x in VOC-limited regime, the relatively low emission ratios of NO_x and VOCs from industry and transportation in Beijing provided convincing evidence for the persistently high O₃ concentrations during 2014-2019. However, the noticeable increase of the O₃ trends in other areas (e.g., Hebei, Tianjin) could be explained by the significant decline in the emission ratios of NO_x and VOCs from anthropogenic emissions especially industry during 2014-2019. Controlling the emission of NO_x can substantially reduce PM_2.5 pollution, but may aggravate O₃ pollution, and thus effective VOC emission control strategies need to be considered for simultaneously controlling O₃ and PM_2.5 pollution in Beijing and other regions of China.

Numerical Simulation of Topography Impact on Transport and Source Apportionment on PM2.5 in a Polluted City in Fenwei Plain

The unique energy structure, high intensity of coal production, and complex terrain, make Fenwei Plain a highly polluted region in China. In this study, we characterized the transport characteristic and sources of PM2.5 (the fraction of particulate matter ≤ 2.5 μm) in Sanmenxia, a polluted city in canyon terrain. The results showed that special topography in Sanmenxia had an important role in the transport of particulates. Sanmenxia is located between two northeast-southwest facing mountains, showing a special local circulation. The local circulation was dominated by a downslope wind at nighttime, while the cross−mountain airflow and zonal wind were dominant during the daytime in the canyon terrain. PM2.5 accumulated near Sanmenxia with the influence of downslope, zonal wind, and topography. The main regional transport paths could be summarized into an eastern path, a northern path, and a western path during the severe haze episodes. The PM2.5 source apportionment revealed by an on-line tracer-tagged of the Nested Air Quality Prediction Model System (NAQPMS) showed that the main regional sources of Sanmenxia were Yuncheng, Sanmenxia, and Weinan. The contribution to PM2.5 concentration in Sanmenxia was 39%, 25%, and 11%, respectively. The northern path had the most important impact on Sanmenxia. The results can provide scientific basis for the establishment of severe haze control in Sanmenxia and regional joint control.

The Different Impacts of Emissions and Meteorology on PM2.5 Changes in Various Regions in China: A Case Study

Emissions and meteorology are significant factors affecting aerosol pollution, but it is not sufficient to understand their relative contributions to aerosol pollution changes. In this study, the observational data and the chemical model (GRAPES_CUACE) are combined to estimate the drivers of PM2.5 changes in various regions (the Beijing–Tianjin–Hebei (BTH), the Central China (CC), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)) between the first month after COVID-19 (FMC_2020) (i.e., from 23 January to 23 February 2020) and the corresponding period in 2019 (FMC_2019). The results show that PM2.5 mass concentration increased by 26% (from 61 to 77 µg m−3) in the BTH, while it decreased by 26% (from 94 to 70 µg m−3) in the CC, 29% (from 52 to 37 µg m−3) in the YRD, and 32% (from 34 to 23 µg m−3) in the PRD in FMC_2020 comparing with FMC_2019, respectively. In the BTH, although emissions reductions partly improved PM2.5 pollution (−5%, i.e., PM2.5 mass concentration decreased by 5% due to emissions) in FMC_2020 compared with that of FMC_2019, the total increase in PM2.5 mass concentration was dominated by more unfavorable meteorological conditions (+31%, i.e., PM2.5 mass concentration increased by 31% due to meteorology). In the CC and the YRD, emissions reductions (−33 and −36%) played a dominating role in the total decrease in PM2.5 in FMC_2020, while the changed meteorological conditions partly worsened PM2.5 pollution (+7 and +7%). In the PRD, emissions reductions (−23%) and more favorable meteorological conditions (−9%) led to a total decrease in PM2.5 mass concentration. This study reminds us that the uncertainties of relative contributions of meteorological conditions and emissions on PM2.5 changes in various regions are large, which is conducive to policymaking scientifically in China.

Spatio-temporal variations in fine particulate matter and evaluation of associated health risk over Pakistan

Human health and the environment are adversely affected by fine particulate matter. By utilizing standard deviation ellipse and trend analyses, we studied the spatial patterns and temporal trends of PM_2.5 over Pakistan from 1998 to 2016. The outcomes of these analyses indicated that PM_2.5 concentrations were considerably amplified in Pakistan, particularly in the provinces of Punjab and Sindh. The areal extent of PM_2.5 concentrations below 15 μg/m³ declined constantly, and the area with PM_2.5 concentrations above 35 μg/m³ increased significantly. The highly affected cities were Lahore, Faisalabad, Multan, Southern Gujranwala, Dera Ghazi Khan, Bahawalpur, Sukkur, and Larkana. Overall, the northwest-southeast axis experienced more rapid variations in the spatial pattern of PM_2.5 than the northeast-southwest axis; similarly, the east-north axis also experienced faster changes in the spatial distribution of this crucial pollutant than the west-south axis. To support nationwide air pollution control, a two-tier level was recommended for allocated regions in Pakistan depending on their PM_2.5 concentrations. From 1998 to 2016, health risks expanded and increased in Pakistan, particularly in Lahore, Karachi, Multan, Gujranwala, Faisalabad, and Hyderabad; these are Pakistan's most populated cities. The outcomes of this study suggest that human health is continuously affected by PM_2.5 in Pakistan, and that a plan of action to combat air pollution is immediately needed. Integr Environ Assess Manag 2021;17:1243-1254. © 2021 SETAC.

Interaction of interregional O3 pollution using complex network analysis

In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O₃ in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring stations between November 2017 and October 2018. This paper uses complex network theory to describe the network structure characteristics of O₃ pollution spatial correlation. On this basis, the node importance method is used to mine the sub-network with the highest spatial correlation in the O₃ network, and use transfer entropy theory to analyse the interaction of pollutants between regions. The results show that the O₃ area of Qilihe District, Lanzhou City can be divided into three parts: the urban street community type areas in urban areas, the township and village type areas in mountain areas and the scattered areas represented by isolated nodes. An analysis of the mutual influence of O₃ between each area revealed that the impact of O₃ on each monitoring station in adjacent areas will vary considerably. Therefore these areas cannot be governed as a whole, and the traditional extensive management measures based on administrative divisions cannot be used to replace all other regional governance measures. There is the need to develop a joint prevention and control mechanism tailored to local conditions in order to improve the accuracy and efficiency of O₃ governance.

Observations of Atmospheric Aerosol and Cloud Using a Polarized Micropulse Lidar in Xi’an, China

A polarized micropulse lidar (P-MPL) employing a pulsed laser at 532 nm was developed by the Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences). The optomechanical structure, technical parameters, detection principle, overlap factor calculation method, and inversion methods of the atmospheric boundary layer (ABL) depth and depolarization ratio (DR) were introduced. Continuous observations using the P-MPL were carried out at Xi’an Meteorological Bureau, and the observation data were analyzed. In this study, we gleaned much information on aerosols and clouds, including the temporal and spatial variation of aerosols and clouds, aerosol extinction coefficient, DR, and the structure of ABL were obtained by the P-MPL. The variation of aerosols and clouds before and after a short rainfall was analyzed by combining time-height-indication (THI) of range corrected signal (RCS) and DR was obtained by the P-MPL with profiles of potential temperature (PT) and relative humidity (RH) detected by GTS1 Digital Radiosonde. Then, the characteristics of tropopause cirrus cloud were discussed using the data of DR, PT, and RH. Finally, a haze process from January 1st to January 5th was studied by using aerosol extinction coefficients obtained by the P-MPL, PT, and RH profiles measured by GTS1 Digital Radiosonde and the time-varying of PM2.5 and PM10 observed by ambient air quality monitor. The source of the haze was simulated by using the NOAA HYSPLIT Trajectory Model.

Mass Concentration, Chemical Composition, and Source Characteristics of PM2.5 in a Plateau Slope City in Southwest China

In order to investigate the seasonal variations in the chemical characteristics of PM2.5 at the plateau slope of a mountain city in southwest China, 178 PM2.5 filters (89 quartz and 89 Teflon samples for PM2.5) were collected to sample the urban air of Wenshan in spring and autumn 2016 at three sites. The mass concentrations, water-soluble inorganic ions, organic and inorganic carbon concentrations, and inorganic elements constituting PM2.5 were determined, principal component analysis was used to identify potential sources of PM2.5, and the backward trajectory model was used to calculate the contribution of the long-distance transmission of air particles to the Wenshan area. The average concentration of PM2.5 in spring and autumn was 44.85 ± 10.99 μg/m3. Secondary inorganic aerosols contributed 21.82% and 16.50% of the total PM2.5 in spring and autumn, respectively. The daily mean value of OC/EC indicated that the measured SOC content was generated by the photochemical processes active during the sampling days. However, elements from anthropogenic sources (Ti, Si, Ca, Fe, Al, K, Mg, Na, Sb, Zn, P, Pb, Mn, As and Cu) accounted for 99.38% and 99.24% of the total inorganic elements in spring and autumn, respectively. Finally, source apportionment showed that SIA, dust, industry, biomass burning, motor vehicle emissions and copper smelting emissions constituted the major components in Wenshan. This study is the first to investigate the chemical characterizations and sources of PM2.5 in Wenshan, and it provides effective support for local governments formulating air pollution control policies.

Effects of Particulate Matter on Healthy Skin: A Comparative Study between High- and Low-Particulate Matter Periods

Background

The influence of airborne particulate matter (PM) on skin has primarily been studied in patients with skin diseases such as atopic dermatitis. Recently, the effect of PM on healthy human skin has gained attention.

Objective

To evaluate the relationship between PM concentration and objective skin changes in healthy subjects.

Methods

This prospective study enrolled 25 healthy volunteers without any skin disease. Data regarding daily meteorological parameters and air pollution were collected during a high-PM period and a low-PM period for 14 days. Environmental and lifestyle factors that might influence skin conditions of subjects were also collected during the study period. Biophysical parameters of the skin such as transepidermal water loss (TEWL), hydration, erythema index, and melanin index were measured. Pores, wrinkles, sebum, and skin tone were evaluated using a facial analysis system.

Results

Mean TEWL value during the high-PM period was significantly higher than that during the low-PM period (10.16 g/m²/h vs. 5.99 g/m²/h; p=0.0005). Mean erythema index was significantly higher in the high-PM period than that in the low-PM period (4.3 vs. 3.42; p=0.038). For facial analysis system indices, uniformity of skin tone was higher in the low-PM period than that in the high-PM period (p<0.0001). In addition, with increasing PM₁₀ and PM_2.5, TEWL also showed increase when other environmental components were constant (regression coefficient [RC]=0.1529, p<0.0001 for PM₁₀; RC=0.2055, p=0.0153 for PM_2.5).

Conclusion

Increased PM concentrations may contribute to disturbed barrier function, increased facial erythema, and uneven skin tone even in healthy human skin.

Regional scenario of air pollution in lockdown due to COVID-19 pandemic: Evidence from major urban agglomerations of India

Air pollution in India during COVID-19 lockdown, which imposed on 25th March to 31st May 2020, has brought a significant improvement in air quality. The present paper mainly focuses on the scenario of air pollution level (PM_2.5, PM₁₀, SO₂, NO₂ and O₃) across 57 urban agglomerations (UAs) of India during lockdown. For analysis, India has been divided into six regions - Northern, Western, Central, Southern, Eastern and North-Eastern. Various spatial statistical modelling with composite air quality index (CAQI) have been utilised to examine the spatial pattern of air pollution level. The result shows that concentration of all air pollutants decreased significantly (except O₃) during lockdown. The maximum decrease is the concentration of NO₂ (40%) followed by PM_2.5 (32%), PM₁₀ (24%) and SO₂ (18%). Among 57 UA's, only five - Panipat (1.00), Ghaziabad (0.76), Delhi (0.74), Gurugram (0.72) and Varanasi (0.71) had least improvement in air pollution level considering entire lockdown period. The outcome of this study has an immense scope to understand the regional scenario of air pollution level and to implement effective strategies for environmental sustainability.

The Effects of Urban Forms on the PM2.5 Concentration in China: A Hierarchical Multiscale Analysis

For a better environment and sustainable development of China, it is indispensable to unravel how urban forms (UF) affect the fine particulate matter (PM2.5) concentration. However, research in this area have not been updated consider multiscale and spatial heterogeneities, thus providing insufficient or incomplete results and analyses. In this study, UF at different scales were extracted and calculated from remote sensing land-use/cover data, and panel data models were then applied to analyze the connections between UF and PM2.5 concentration at the city and provincial scales. Our comparison and evaluation results showed that the PM2.5 concentration could be affected by the UF designations, with the largest patch index (LPI) and landscape shape index (LSI) the most influential at the provincial and city scales, respectively. The number of patches (NP) has a strong negative influence (-0.033) on the PM2.5 concentration at the provincial scale, but it was not statistically significant at the city scale. No significant impact of urban compactness on the PM2.5 concentration was found at the city scale. In terms of the eastern and central provinces, LPI imposed a weighty positive influence on PM2.5 concentration, but it did not exert a significant effect in the western provinces. In the western cities, if the urban layout were either irregular or scattered, exposure to high PM2.5 pollution levels would increase. This study reveals distinct ties of the different UF and PM2.5 concentration at the various scales and helps to determine the reasonable UF in different locations, aimed at reducing the PM2.5 concentration.

Economic policy choice of governing haze pollution: evidence from global 74 countries

Haze pollution not only has a huge impact on economic development but also seriously damages the health of residents, which has attracted the attention of many countries and scholars. The geographical detector model and the panel quantile regression model are used in combination to analyze the socio-economic driving factors of haze pollution from 2010 to 2015 for 74 significantly representative countries. The main results are as follows: (1) Industrial structure is the main factor affecting the haze concentration, followed by economic growth and research and development (R&D) intensity. (2) Government influence and industrial structure will significantly aggravate haze pollution, whereas the energy intensity and economic growth have an inhibitory effect on haze concentration. Countries with severe haze pollution should focus on upgrading their industrial structure and avoiding energy rebound. (3) Urbanization, foreign investment, and R&D intensity have different effects on the haze concentration among countries with different pollution levels. Specifically, the relationship between economic growth and pollution is inverted N-shaped in countries with medium haze concentration, whereas in other countries, it is positive N-shaped. Countries should actively leverage the agglomeration effect of high-density urban populations and focus on the introduction of high-quality foreign capital.

Explore Regional PM2.5 Features and Compositions Causing Health Effects in Taiwan

Chemical compositions of atmospheric fine particles like PM2.5 prove harmful to human health, particularly to cardiopulmonary functions. Multifaceted health effects of PM2.5 have raised broader, stronger concerns in recent years, calling for comprehensive environmental health-risk assessments to offer new insights into air-pollution control. However, there have been few studies adopting local air-quality-monitoring datasets or local coefficients related to PM2.5 health-risk assessment. This study aims to assess health effects caused by PM2.5 concentrations and metal toxicity using epidemiological and toxicological methods based on long-term (2007-2017) hourly monitoring datasets of PM2.5 concentrations in four cities of Taiwan. The results indicated that (1) PM2.5 concentrations and hazardous substances varied substantially from region to region, (2) PM2.5 concentrations significantly decreased after 2013, which benefited mainly from two actions against air pollution, i.e., implementing air-pollution-control strategies and raising air-quality standards for certain emission sources, and (3) under the condition of low PM2.5 concentrations, high health risks occurred in eastern Taiwan on account of toxic substances adsorbed on PM2.5 surface. It appears that under the condition of low PM2.5 concentrations, the results of epidemiological and toxicological health-risk assessments may not agree with each other. This raises a warning that air-pollution control needs to consider toxic substances adsorbed in PM2.5 and region-oriented control strategies are desirable. We hope that our findings and the proposed transferable methodology can call on domestic and foreign authorities to review current air-pollution-control policies with an outlook on the toxicity of PM2.5.

Impacts of the COVID-19 event on the NOx emissions of key polluting enterprises in China

The unprecedented cessation of human activities during the COVID-19 pandemic has affected China's industrial production and NOx emissions. Quantifying the changes in NOx emissions resulting from COVID-19 and associated governmental control measures is crucial to understanding its impacts on the environment. Here, we divided the research timeframe into three periods: the normal operation period (P1), the Spring Festival period (P2), and the epidemic period following the Spring Festival (P3). We then calculated the NOx operating vent numbers and emission concentrations of key polluting enterprises in 29 provinces and 20 industrial sectors and compared the data for the same periods in 2020 and 2019 to obtain the impacts of COVID-19 on industrial NOx emissions. We found that spatially, from P1 to P2 in 2020, the operating NOx vent numbers in North China changed the most, with a relative change rate of -33.84%. Comparing the operating vent numbers in P1 and P3, East China experienced the largest decrease, approximately -32.72%. Among all industrial sectors, the mining industry, manufacturing industry, power, heat, gas, and water production and supply industry, and the wholesale and retail industry, were the most heavily influenced. In general, the operating vent numbers of key polluting enterprises in China decreased by 24.68%, and the standardized NOx (w)_5-day decreased by an average of -9.54 ± -6.00 due to the COVID-19 pandemic. The results suggest that COVID-19 significantly reduced the NOx emission levels of the key polluting enterprises in China.

Research on the strategic interaction of China's regional air pollution regulation: spatial interpretation of "incomplete implementation" of regulatory policies

Research on the current strategic interaction of local governments in air pollution control is a key breakthrough. Based on the theory of Chinese style decentralization, this paper puts forward a theoretical framework to explain the incomplete enforcement of air pollution regulation. Using the panel data of 30 provinces in China from 2004 to 2016, this study employs spatial Durbin model, empirically tested the inter-regional strategic interaction of air pollution regulation, and further explores the effect of performance assessment indicators on this strategic interaction. The main conclusions of this paper are as follows: (1) Empirical results confirm that adjacent provinces do exist strategic interaction of air pollution regulation. Furthermore, the strategic interaction of air pollution regulation belongs to complementarities. (2) Meanwhile, from a regional perspective, due to the low level of economic development stock and the low level of air pollution, the interaction effect of air pollution regulation strategies in northwestern region is weaker than that in southeastern region. (3) In addition, under the national sample and the southeast sample, the air environment performance assessment indicators weaken the inter-regional strategic interaction of air pollution regulation, and economic performance assessment indicators on the contrary.

Understanding sources of fine particulate matter in China

Fine particulate matter has been a major concern in China as it is closely linked to issues such as haze, health and climate impacts. Since China released its new national air quality standard for fine particulate matter (PM2.5) in 2012, great efforts have been put into reducing its concentration and meeting the standard. Significant improvement has been seen in recent years, especially in Beijing, the capital city of China. This paper reviews how China understands its sources of fine particulate matter, the major contributor to haze, and the most recent findings by researchers. It covers the characteristics of PM2.5 in China, the major methods to understand its sources such as emission inventory and measurement networks, the major research programmes in air quality research, and the major measures that lead to successful control of fine particulate matter pollution. A great example of linking scientific findings to policy is the control of coal combustion from the residential sector in northern China. This review not only provides an overview of the fine particulate matter pollution problem in China, but also its experience of air quality management, which may benefit other countries facing similar issues. This article is part of a discussion meeting issue 'Air quality, past present and future'.

Does environmental regulation reduce China's haze pollution? An empirical analysis based on panel quantile regression

Haze pollution in China is very serious and has become the source of mortality, affecting the health and lives of residents. The Chinese government adopts different policy measures to reduce haze pollution. The impact of different types of environmental regulations on haze pollution has become a hot topic for academics and government departments. Based on panel data from 2005–2017, this paper studies the effect of different types of environmental regulations on haze pollution in 30 provinces of China using a panel quantile model. The results show that when haze pollution changes from a low quantile to a high quantile, the marginal impact of command-and-control environmental regulation on haze pollution changes from 0.122 to -0.332. Command-and-control environmental regulation can reduce haze pollution, but its impact is not significant. The main reason for this finding is that environmental law enforcement is not strict. The marginal impact of economically restrained environmental regulation on haze pollution changes from -14.389 to 49.939. Economically restrained environmental regulation can reduce haze pollution in low quantiles, but not in high quantiles. The collection of sewage charges fees is far less than the total profit, which has no deterrent effect on enterprises. The marginal impact of public participation in environmental regulation on haze pollution changes from 0.154 to -0.002. Public participation in environmental regulation cannot reduce haze pollution in low quantiles, but can in high quantiles; however its impact becomes insignificant. This study reveals the quantile-based discrepancy in the effect of environmental regulation on haze pollution, and offers a new perspective for research on the effects of environmental regulation.

Characteristics and trends of childhood cancer in Pudong, China, 2002-2015

Background

With the growing threat of cancer to children’s health, it is necessary to analyze characteristics and trends of childhood cancer to formulate better cancer prevention strategies.

Methods

Data on the 430 children with cancer during 2002–2015 were collected from the Pudong Cancer Registry, diagnosed with the International Classification of Diseases for Oncology and categorized according to the International Classification of Childhood Cancer. The incidence rate, trends over time, and survival of patients grouped by sex, age, and region were explored using the Kaplan-Meier, Cox regression, and Joinpoint Regression models.

Results

The crude childhood cancer incidence and world age-standardized incidence rate (ASR) were 115.1/1,000,000 and 116.3/1,000,000 person-years. The two most frequent cancers were leukemia (136/430, 31.63%, ASR, 37.8/1,000,000 person-years) and central nervous system (CNS) tumors (86/430, 20.00%, ASR, 22.9/1,000,000 person-years). Our findings indicate that the survival rate for children between 10 and 15 years of age was higher than that for 5–10; and the survival rate for children who had leukemia was higher than that of children with CNS tumors. However, the overall incidence of childhood cancer, and leukemia, CNS tumors remained relatively stable in Pudong between 2002 and 2015.

Conclusions

The incidence and survival rate for childhood cancer patients varied by age and cancer type. The overall trends of childhood cancer incidence remained relatively stable in Pudong from 2002 to 2015 even though socioeconomic development has been unprecedentedly fast in this region.

Investigation of the impacts of urban vegetation loss on the ecosystem service of air pollution mitigation in Karaj metropolis, Iran

The present study aims to investigate the relationship between reduced air pollution and ecosystem services in Karaj metropolis, Iran. To the end, the trends in the concentrations of O₃, NO₂, CO, SO₂, PM₁₀, and PM_2.5 as the main atmospheric pollutants of Karaj were studied. Five time series models of autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) were used to predict changes in air pollutant concentrations. Air pollution zoning is conducted via ArcGIS_10.3 by using spline tension interpolation method. Then, normalized difference vegetation index (NDVI) was obtained from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to analyze vegetation dynamics as an index of ecosystem functioning. NDVI thresholds were selected to present guidelines for qualitative and quantitative changes in green cover and were divided into five different categories. Based on the results, AR (1) and ARIMA (1,2,1) were recognized as appropriate models for predicting the concentration of air pollutants in the study area. A decrease in very dense vegetation coverage and increase in poor vegetation areas, followed by an increase in air pollution, revealed that the loss of urban green coverage and decreased ecosystem services were positively related. Furthermore, the expansion of urban lands toward the north and the west from the baseline to future condition led to great changes in the land cover and losses in vegetation along these axes, which finally resulted in increased air pollution in these areas. Thus, the results of this study can be directly used in decision-making in the area of air pollution.

Mining sequential patterns of PM2.5 pollution between 338 cities in China

Serious PM2.5 air pollution has persistently plagued and endangered most urban areas in China in recent years, and targeted policies are necessary to improve urban air quality ranging from macro policy (national level) to medium policy (city level) to micro policy (site specific). However, the macro-pattern study of air pollution between Chinese cities is inadequate, and not conducive to the formulation of macro-policy. To bridge this gap, we applied a sequential pattern mining algorithm to analyze the spatial-temporal patterns of PM2.5 pollution across Chinese cities during the period 2015 to 2018. The sequential patterns were collected from three levels of granularity on geographic areas and ten temporal scenarios covering time intervals from 10 to 100 h. Many underlying associative relationships were revealed between different cities by the mined patterns. The patterns were heterogeneous and presented five characteristics (i.e., clustering, symmetry, imbalance, decay, and stability). Each of the urban areas under investigation at different granularities was analyzed to identify the occurrence of associative relationships between it and other urban areas; moreover, we determined the degree of severity of such relationships. Our research results provide solid data that can be used as a reference by the various levels of Chinese governments for decision-making; overall, they can be used to improve the design of joint policies to prevent and control PM2.5 pollution in Chinese urban areas.

Effects of Meteorological Factors and Anthropogenic Precursors on PM2.5 Concentrations in Cities in China

Fine particulate matter smaller than 2.5 μm (PM2.5) in size can significantly affect human health, atmospheric visibility, climate, and ecosystems. PM2.5 has become the major air pollutant in most cities of China. However, influencing factors and their interactive effects on PM2.5 concentrations remain unclear. This study used a geographic detector method to quantify the effects of anthropogenic precursors (AP) and meteorological factors on PM2.5 concentrations in cities of China. Results showed that impacts of meteorological conditions and AP on PM2.5 have significant spatio-temporal disparities. Temperature was the main influencing factor throughout the whole year, which can explain 27% of PM2.5 concentrations. Precipitation and temperature were primary impacting factors in southern and northern China, respectively, at the annual time scale. In winter, AP had stronger impacts on PM2.5 in northern China than in other seasons. Ammonia had stronger impacts on PM2.5 than other anthropogenic precursors in winter. The interaction between all factors enhanced the formation of PM2.5 concentrations. The interaction between ammonia and temperature had strongest impacts at the national scale, explaining 46% (q = 0.46) of PM2.5 concentrations. The findings comprehensively elucidated the relative importance of driving factors in PM2.5 formation, which can provide basic foundations for understanding the meteorological and anthropogenic influences on the concentration patterns of PM2.5.

The haze extreme co-movements in Beijing-Tianjin-Hebei region and its extreme dependence pattern recognitions

Extreme haze was often observed at many locations in Beijing-Tianjin-Hebei region within several hours when they occurred, which is referred to as extreme co-movements and extreme dependence in statistics. This article applies tail quotient correlation coefficient to explore the temporal and spatial extreme dependence patterns of haze in this region. Hourly PM2.5 station-level data during 2014-2018 are used, and the results show that the tail quotient correlation coefficient between stations increases with month. Specifically, the simultaneous extreme dependence was strong in the fourth season, while the haze was severe. In the first season, while the haze was also severe, the extreme hazes only show strong co-movements with a time difference. These observations lead to the study of two special scenarios, that is, the concurrence/extreme dependence of the worst extreme haze and its lag effects. City clusters suffering simultaneous extreme haze or with certain time difference as well as the most frequently co-movement cities are identified. The extreme co-movements of these cities and the reasons for their occurrences have strong implications for improving the PM2.5 joint prevention and control in the Beijing-Tianjin-Hebei region. The importance of lag effects is also reflected in the precedence order of the extreme haze's appearance. It is especially useful when setting the mechanism of the early warning system which can be triggered by the first appearance of extreme haze. The precedence orders also avail in investigating the transmission path of the haze, based on which more precise meteorological models can be made to benefit the haze forecasting of the region.

Joint Governance Regions and Major Prevention Periods of PM2.5 Pollution in China Based on Wavelet Analysis and Concentration-Weighted Trajectory

China has made some progress in controlling PM2.5 (particulate matter with an aerodynamic diameter of ≤2.5 μm) pollution, but there are still some key areas that need further strengthening. Considering that excessive prevention and control efforts affect economic development, this paper combined an empirical orthogonal function, a continuous wavelet transform, and a concentration-weighted trajectory method to study joint regional governance during key pollution periods to provide suggestions for the efficient control of PM2.5. The results from our panel of data of PM2.5 in China from 2016 to 2018 could be decomposed into two modes. In the first mode, the pollution center was in central Shaanxi Province, and the main eruption period was from November to January of the following year. As the center of this region, Xi’an should cooperate with the four cities in eastern Sichuan (Nanchong, Guangan, Bazhong, and Dazhou) to control PM2.5, since the eruption occurred in this area. Moreover, governance should last for at least two cycles, where one cycle is at least 23 days. The pollution center of the second mode was in the western part of Xinjiang. Therefore, after the prevention and control efforts during the first mode are completed, the regional city of Kashgar should continue to build a joint governance zone for PM2.5 along the Tianshan mountains in the east, focusing on prevention and control over two cycles (where one cycle is 28 days).

Effect of the wetland environment on particulate matter and dry deposition

In Beijing, particulate matter (PM) in the atmosphere, especially PM_2.5 and PM₁₀, have attracted public attention because of its adverse effects. A series of studies have investigated the sources and spatial-temporal variation of PM. Wetland has been reported to own the capacity of resolving air problem. To examine the characteristics of the particulate matter in wetlands, the diurnal variation of PM_2.5 and PM₁₀ concentrations with respect to two heights (i.e. 1.5 and 10 m, respectively) and three meteorological factors (i.e. wind speed, temperature, and relative humidity, respectively) was monitored in the Cuihu National Wetland Park in Beijing, and the dry deposition velocity and flux were analysed using the above-mentioned data. Results indicated that (1) As for diurnal variation, the PM concentration constantly decreased at 07:00-16:00 and gradually increased at 16:00-18:00. The maximum instantaneous concentration was observed at 07:00-10:00, while the minimum instantaneous concentration was observed at 13:00-16:00. (2) The annual concentration variation of PM followed the order of dry period > wet period > normal period. (3) The particulate concentrations at 10 m were always greater than those at 1.5 m. (4) The PM concentration was positively correlated to the relative humidity and negatively correlated to the temperature. Wind speed exhibited a complex effect on PM concentration. (5) The regulation of dry deposition efficiency followed the order of spring > winter > summer. (6) Wind speed strongly and positively affected the dry deposition velocity of PM10. The effects of temperature and relative humidity on dry deposition were uncertain.

Spatial homogeneity and heterogeneity of ambient air pollutants in Tehran

To investigate spatial inequality of ambient air pollutants and comparison of their heterogeneity and homogeneity across Tehran, the following quantitative indicators were utilized: coefficient of divergence (COD), the 90^th percentile of the absolute differences between ambient air pollutant concentrations and coefficient of variation (CV). Real-time hourly concentrations of particulate matter (PM) and gaseous air pollutants (GAPs) of twenty-two air quality monitoring stations (AQMSs) were obtained from Tehran Air Quality Control Company (TAQCC) in 2017. Annual mean concentrations of PM_2.5, PM_10-2.5, and PM₁₀ (PM_X) ranged from 21.7 to 40.5, 37.3 to 75.0 and 58.0 to 110.4 μg m^-3, respectively. Annual mean PM_2.5 and PM₁₀ concentrations were higher than the World Health Organization air quality guideline (WHO AQG) and national standard levels. NO₂, O₃, SO₂ and CO annual mean concentrations ranged from 27.0 to 76.8, 15.5 to 25.1, 4.6 to 12.2 ppb, and 1.9 to 3.8 ppm over AQMSs, respectively. Our generated spatial maps exhibited that ambient PM_X concentrations increased from the north into south and south-western areas as the hotspots of ambient PM_X in Tehran. O₃ hotspots were observed in the north and south-west, while NO₂ hotspots were in the west and south. COD values of PM_X demonstrated more results lower than the 0.2 cut off compared to GAPs; indicating high to moderate spatial homogeneity for PM_X and moderate to high spatial heterogeneity for GAPs. Regarding CV approach, the spatial variabilities of air pollutants followed in the order of O₃ (87.3%) > SO₂ (65.2%) > CO (61.8%) > PM_10-2.5 (52.5%) > PM_2.5 (48.9%) > NO₂ (48.1%) > PM₁₀ (42.9%), which were mainly in agreement with COD results, except for NO₂. COD values observed a statistically (P < 0.05) positive correlation with the values of the 90^th percentile across AQMSs. Our study, for the first time, highlights spatial inequality of ambient PM_X and GAPs in Tehran in detail to better facilitate establishing new intra-urban control policies.

Predicting gestational personal exposure to PM2.5 from satellite-driven ambient concentrations in Shanghai

BACKGROUND

It has been widely reported that gestational exposure to fine particulate matters (PM_2.5) is associated with a series of adverse birth outcomes. However, the discrepancy between ambient PM_2.5 concentrations and personal PM_2.5 exposure would significantly affect the estimation of exposure-response relationship.

OBJECTIVE

Our study aimed to predict gestational personal exposure to PM_2.5 from the satellite-driven ambient concentrations and analyze the influence of other potential determinants.

METHOD

We collected 762 72-h personal exposure samples from a panel of 329 pregnant women in Shanghai, China as well as their time-activity patterns from Feb 2017 to Jun 2018. We established an ambient PM_2.5 model based on MAIAC AOD at 1 km resolution, then used its output as a major predictor to develop a personal exposure model.

RESULTS

Our ambient PM_2.5 model yielded a cross-validation R² of 0.96. Personal PM_2.5 exposure levels were almost identical to the corresponding ambient concentrations. After adjusting for time-activity patterns and meteorological factors, our personal exposure has a CV R² of 0.76.

CONCLUSION

We established a prediction model for gestational personal exposure to PM_2.5 from satellite-based ambient concentrations and provided a methodological reference for further epidemiological studies.

Association of School Residential PM2.5 with Childhood High Blood Pressure: Results from an Observational Study in 6 Cities in China

OBJECTIVE

To investigate the association of long-term PM_2.5 exposure with blood pressure (BP) outcomes in children aged 6-18 years, and to examine the population attributable risk (PAR) of PM_2.5 exposure.

METHODS

A total of 53,289 participants aged 6-18 years with full record of age, sex, BP, height, and local PM_2.5 exposure from a cross-sectional survey conducted in 6 cities of China in 2013 were involved in the present study. PM_2.5 data from 18 January 2013 to 31 December 2013 were obtained from the nearest environmental monitoring station for each selected school. Two-level linear and logistic regression models were used to evaluate the influence of PM_2.5 on children's BP, and PAR was calculated in each sex and age group.

RESULTS

Participants had a mean age of 10.8 (standard deviation: 3.4) years at enrollment, 51.7% of them were boys. U-shaped trends along with increased PM_2.5 concentration were found for both systolic blood pressure (SBP) and diastolic blood pressure (DBP), with the thresholds of 57.8 and 65.0 μg/m³, respectively. Both increased annual mean of PM_2.5 concentration and ratio of polluted days were associated with increased BP levels and high blood pressure (HBP), with effect estimates for BP ranging from 2.80 (95% CI: -0.51, 6.11) mmHg to 5.78 (95% CI: 2.32, 9.25) mmHg for SBP and from 0.77 (95% CI: -1.98, 3.52) mmHg to 2.66 (-0.35, 5.66) mmHg for DBP, and the odds ratios for HBP from 1.21 (0.43, 3.38) to 1.92 (0.65, 5.67) in the highest vs. the lowest quartiles. Overall, 1.16% of HBP in our participants could be attributed to increased annual mean of PM_2.5 concentration, while 2.82% could be attributed to increased ratio of polluted days. These proportions increased with age.

CONCLUSIONS

The association between long-term PM_2.5 exposure and BP values appeared to be U-shaped in Chinese children aged 6-18 years, and increased PM_2.5 exposure was associated with higher risk of HBP.

Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction

People have been suffering from air pollution for a decade in China, especially from PM_2.5 (particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has great practical significance. In this paper, we propose a data-driven model, called as long short-term memory - fully connected (LSTM-FC) neural network, to predict PM_2.5 contamination of a specific air quality monitoring station over 48 h using historical air quality data, meteorological data, weather forecast data, and the day of the week. Our predictive model consists of two components: (1) Using a long short-term memory (LSTM)-based temporal simulator to model the local variation of PM_2.5 contamination and (2) Using a neural network-based spatial combinatory to capture spatial dependencies between the PM_2.5 contamination of central station and that of neighbor stations. We evaluate our model on a dataset containing records of 36 air quality monitoring stations in Beijing from 2014/05/01 to 2015/04/30 and compare it with artificial neural network (ANN) and long short-term memory (LSTM) models on the same dataset. The results show that our LSTM-FC neural network model gives a better predictive performance.

Spatial-Temporal Evolution of PM2.5 Concentration and its Socioeconomic Influence Factors in Chinese Cities in 2014⁻2017

PM_2.5 is a main source of China’s frequent air pollution. Using real-time monitoring of PM_2.5 data in 338 Chinese cities during 2014–2017, this study employed multi-temporal and multi-spatial scale statistical analysis to reveal the temporal and spatial characteristics of PM_2.5 patterns and a spatial econometric model to quantify the socio-economic driving factors of PM_2.5 concentration changes. The results are as follows: (1) The annual average value of PM_2.5 concentration decreased year by year and the monthly average showed a U-shaped curve from January to December. The daily mean value of PM_2.5 concentration had the characteristics of pulse-type fluctuation and the hourly variation presented a bimodal curve. (2) During 2014–2017, the overall PM_2.5 pollution reduced significantly, but that of more than two-thirds of cities still exceeded the standard value (35 μg/m³) regulated by Chinese government. PM_2.5 pollution patterns showed high values in central and eastern Chinese cities and low values in peripheral areas, with the distinction evident along the same line that delineates China’s uneven population distribution. (3) Population agglomeration, industrial development, foreign investment, transportation, and pollution emissions contributed to the increase of PM_2.5 concentration. Urban population density contributed most significantly while economic development and technological progress reduced PM_2.5 concentration. The results also suggest that China in general remains a “pollution shelter” for foreign-funded enterprises.

Installation Planning in Regional Thermal Power Industry for Emissions Reduction Based on an Emissions Inventory

Exploring suitable strategies for air pollution control, while still maintaining sustainable development of the thermal power industry, is significant for the improvement of environmental quality and public health. This study aimed to establish a coupling relationship between installed capacity versus energy consumption and pollutant emissions, namely the installed efficiency, and to further provide ideas and methods for the control of regional air pollutants and installation planning. An inventory of 338 installed thermal power units in the Jing-Jin-Ji Region in 2013 was established as a case study, and comparisons were made by clustering classification based on the installed efficiencies of energy consumption and pollutant emissions. The results show that the thermal power units were divided into five classes by their installed capacity: 0⁻50, 50⁻200, 200⁻350, 350⁻600, and 600+ MW. Under the energy conservation and emissions reduction scenario, with the total installed capacity and the power generation generally kept constant, the coal consumption was reduced by 17.1 million tons (8.7%), and the total emissions were reduced by 79.8% (SO₂), 84.9% (NO_x), 60.9% (PM), and 59.5% (PM_2.5).

Spatio-temporal variations and factors of a provincial PM2.5 pollution in eastern China during 2013-2017 by geostatistics

Fine particulate matter (PM_2.5) is a typical air pollutant and has adverse health effects across the world, especially in the rapidly developing China due to significant air pollution. The PM_2.5 pollution varies with time and space, and is dominated by the locations owing to the differences in geographical conditions including topography and meteorology, the land use and the characteristics of urbanization and industrialization, all of which control the pollution formation by influencing the various sources and transport of PM_2.5. To characterize these parameters and mechanisms, the 5-year PM_2.5 pollution patterns of Jiangsu province in eastern China with high-resolution was investigated. The Kriging interpolation method of geostatistical analysis (GIS) and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were conducted to study the spatial and temporal distribution of air pollution at 110 sites from national air quality monitoring network covering 13 cities. The PM_2.5 pollution of the studied region was obvious, although the annual average concentration decreased from previous 72 to recent 50 μg m⁻³. Evident temporal variations showed high PM_2.5 level in winter and low in summer. Spatially, PM_2.5 level was higher in northern (inland, heavy industry) than that in eastern (costal, plain) regions. Industrial sources contributed highest to the air pollution. Backward trajectory clustering and potential source contribution factor (PSCF) analysis indicated that the typical monsoon climate played an important role in the aerosol transport. In summer, the air mass in Jiangsu was mainly affected by the updraft from near region, which accounted for about 60% of the total number of trajectories, while in winter, the long-distance transport from the northwest had a significant impact on air pollution.

Fine Particulate Air Pollution and Hospital Utilization for Upper Respiratory Tract Infections in Beijing, China

Few studies have examined the association between fine particulate matter (PM_2.5) and upper respiratory tract infections (URTI) in urban cities. The principal aim of the present study was to evaluate the short-term impact of PM_2.5 on the incidence of URTI in Beijing, China. Data on hospital visits due to URTI from 1 October 2010 to 30 September 2012 were obtained from the Beijing Medical Claim Data for Employees, a health insurance database. Daily PM_2.5 concentration was acquired from the embassy of the United States of America (US) located in Beijing. A generalized additive Poisson model was used to analyze the effect of PM_2.5 on hospital visits for URTI. We found that a 10 μg/m³ increase in PM_2.5 concentration was associated with 0.84% (95% CI, 0.05⁻1.64%) increase in hospital admissions for URTI at lag 0⁻3 days, but there were no significant associations with emergency room or outpatient visits. Compared to females, males were more likely to be hospitalized for URTI when the PM_2.5 level increased, but other findings did not differ by age group or gender. The study suggests that short-term variations in PM_2.5 concentrations have small but detectable impacts on hospital utilization due to URTI in adults.