Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001-2011

Development of a noise-assisted multivariate empirical mode decomposition framework for characterizing PM 2.5 air pollution in Taiwan and its relation to hydro-meteorological factors

To better understand air pollution problems, the relationships between PM_2.5 and hydro-meteorological variables are studied using a state-of-the-art multivariate nonlinear and non-stationary filtering method, noise-assisted multivariate empirical mode decomposition (NAMEMD), and the time-dependent intrinsic correlation (TDIC) algorithm. Three characteristic scales (annual, diurnal and semi-diurnal) are shown to be significant to PM2.5 characterization, based on using NAMEMD filtering. Temporal fluctuations of local correlations among PM_2.5 and hydro-meteorological variables are presented. On diurnal and semi-diurnal scales, seasonal variation of the local correlation between temperature and humidity is observed. A combined wind speed and direction analysis can be conducted using the NAMEMD-based algorithm. The pollutant roses that are generated from the reconstructed wind directions reveal the sources of PM_2.5 on different scales. PM_2.5 is found to be related to land breeze at the diurnal scale and to winter monsoons at the annual scale. The scale-dependent wind direction that contributes to the increase of PM_2.5 can be identified.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

Residential greenness, air pollution and psychological well-being among urban residents in Guangzhou, China

China's rapid urbanization has led to an increasing level of exposure to air pollution and a decreasing level of exposure to vegetation among urban populations. Both trends may pose threats to psychological well-being. Previous studies on the interrelationships among greenness, air pollution and psychological well-being rely on exposure measures from remote sensing data, which may fail to accurately capture how people perceive vegetation on the ground. To address this research gap, this study aimed to explore relationships among neighbourhood greenness, air pollution exposure and psychological well-being, using survey data on 1029 adults residing in 35 neighbourhoods in Guangzhou, China. We used the Normalized Difference Vegetation Index (NDVI) and streetscape greenery (SVG) to assess greenery exposure at the neighbourhood level, and we distinguished between trees (SVG-tree) and grasses (SVG-grass) when generating streetscape greenery exposure metrics. We used two objective (PM_2.5 and NO₂ concentrations) measures and one subjective (perceived air pollution) measure to quantify air pollution exposure. We quantified psychological well-being using the World Health Organization Well-Being Index (WHO-5). Results from multilevel structural equation models (SEM) showed that, for parallel mediation models, while the association between SVG-grass and psychological well-being was completely mediated by perceived air pollution and NO₂, the relationship between SVG-tree and psychological well-being was completely mediated by ambient PM_2.5, NO₂ and perceived air pollution. None of three air pollution indicators mediated the association between psychological well-being and NDVI. For serial mediation models, measures of air pollution did not mediate the relationship between NDVI and psychological well-being. While the linkage between SVG-grass and psychological well-being scores was partially mediated by NO₂-perceived air pollution, SVG-tree was partially mediated by both ambient PM_2.5-perceived air pollution and NO₂-perceived air pollution. Our results suggest that street trees may be more related to lower air pollution levels and better mental health than grasses are.

Emission characteristics of size distribution, chemical composition and light absorption of particles from field-scale crop residue burning in Northeast China

Crop residue burning in China increased significantly in the last decade, especially it took up a majority in Northeast China, which plays an important role of severe haze pollution. Hence, two main types of crop residues (corn and rice straw) were chosen to characterize the particle number concentration, chemical components of fine particulate matter and optical properties of carbonaceous aerosols by a suite of fast-response online portable instruments, together with offline sampling and analysis, during the field-based combustion experiments in Northeast China. For the range of 250 and 2500 nm, more particles were emitted from rice straw burning than those from corn straw burning, and the time-averaged number concentration of particles during the flaming process was approximately 2 times higher than that during the smoldering process for these two straws. Organic carbon (OC), elemental carbon (EC) and water-soluble ions were the most abundant components and accounted for 42.5 ± 7.5%, 7.7 ± 1.7% and 18.0 ± 3.4% of the PM_2.5, respectively. Furthermore, rice straw burning emitted higher OC and lower Cl^- and K⁺ than those from corn straw burning. The average absorption Ångström exponent (AAE) of carbonaceous aerosols was 2.1 ± 0.3, while the AAE of brown carbon (BrC) was 4.7 ± 0.4 during the whole burning process. On average, BrC contributed to 63% and 20% of the total light absorption at 375 nm and 625 nm, respectively. Parameterization of BrC absorption revealed that the fraction of absorption from BrC has a reasonably good correlation with EC/OC (-0.84) and AAE (0.94) at 375 nm. Generally, combustion conditions can affect the optical properties of carbonaceous aerosols, and a negative correlation (-0.77) was observed between the AAE and modified combustion efficiency; in addition, the percentage of absorption due to BrC were lower at the flaming phase.

The cellular effects of PM2.5 collected in Chinese Taiyuan and Guangzhou and their associations with polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs and hydroxy-PAHs

Numerous studies have demonstrated adverse effects on human health after exposure to fine particulate matter (PM_2.5). However, it is still not clear how the toxicological effects and the health risks vary among PM samples of different compositions and concentrations. In this study, we examined effects of region- and season-dependent differences of PM_2.5 on cytotoxicity, and the contributions of PAHs, nitro-PAHs (N-PAHs) and hydroxy-PAHs (OH-PAHs) to PM_2.5 toxicity by determining different toxicological indicators in three lung cell lines. The results illustrated significant differences in components concentrations and biological responses elicited by PM_2.5 collected in different cities and seasons. The concentrations of most PAHs, N-PAHs and OH-PAHs were much higher in Taiyuan than in Guangzhou. PM_2.5 from Taiyuan exhibited lower cell viability and higher reactive oxygen species (ROS) and interleukin-6 (IL-6) release on lung cells than those from Guangzhou. Specifically, PM_2.5 collected in summer from Taiyuan caused higher levels of pro-inflammatory responses and oxidative potential than those collected in winter. The correlation analysis between 19 PAHs, 17 N-PAHs and 12 OH-PAHs and the measured indicators demonstrated that PAHs were more related to PM_2.5-induced CCK-8 cytotoxicity and IL-6 release in Taiyuan while N-PAHs and OH-PAHs were more related to PM_2.5-induced CCK-8 cytotoxicity and dithiothreitol (DTT)-based redox activity in Guangzhou, suggesting that the toxicity of PM_2.5 from Taiyuan was mostly correlated with PAHs while the toxicity of PM_2.5 from Guangzhou was closely associated with N-PAHs and OH-PAHs. These results revealed that composition differences in PM_2.5 from different regions and seasons significantly accounted for the differences of their toxicological effects.

Spatial Association Pattern of Air Pollution and Influencing Factors in the Beijing-Tianjin-Hebei Air Pollution Transmission Channel: A Case Study in Henan Province

The Beijing–Tianjin–Hebei (BTH) air pollution transmission channel and its surrounding areas are of importance to air pollution control in China. Based on daily data of air quality index (AQI) and air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) from 2015 to 2016, this study analyzed the spatial and temporal characteristics of air pollution and influencing factors in Henan Province, a key region of the BTH air pollution transmission channel. The result showed that non-attainment days and NAQI were slightly improved at the provincial scale during the study period, whereas that in Hebi, Puyang, and Anyang became worse. PM_2.5 was the largest contributor to the air pollution in all cities based on the number of non-attainment days, but its mean frequency decreased by 21.62%, with the mean occurrence of O₃ doubled. The spatial distribution of NAQI presented a spatial agglomeration pattern, with high-high agglomeration area varying from Jiaozuo, Xinxiang, and Zhengzhou to Anyang and Hebi. In addition, the NAQI was negatively correlated with sunshine duration, temperature, relative humidity, wind speed, and positively to atmospheric pressure and relative humidity in all four clusters, whereas relationships between socioeconomic factors and NAQI differed among them. These findings highlight the need to establish and adjust regional joint prevention and control of air pollution as well as suggest that it is crucially important for implementing effective strategies for O₃ pollution control.

Atmospheric Pollution Mapping of the Yangtze River Basin: An AQI-based Weighted Co-word Analysis

The purpose of this paper is to analyze the characteristics and human effects of atmospheric pollution in the Yangtze River Basin (YRB). An AQI(Air Quality Index)-based weighted co-word method is applied to explore the characteristics of keywords taken from the data, using authoritative media sources and government reports. Hierarchical clustering techniques are utilized to classify and visualize the keywords and display the different types of incidents. The results reveal the following four main clusters: enterprise pollution, coal-burning pollution, traffic pollution, and air pollutants. Cluster 1 is divided into 7 sub-clusters to offer powerful insight into the structural characteristics of industrial activities. This study is one of the first attempts to use a bibliometric approach to visualize the underlying and interconnected sub-clusters from grey data. It also provides an atmospheric pollution mapping for formulating government policies by understanding the human effects of air pollution incidents.

Characteristics of Surface Ozone in Five Provincial Capital Cities of China during 2014–2015

Ozone (O3) pollution has become an increasing concern in China since elevated surface O3 concentrations were observed in recent years. In this study, five provincial cities (Beijing, Shanghai, Guangzhou, Xi’an, and Hefei) located in different regions of China were selected to study the spatiotemporal variations and affecting factors of O3 concentrations during 2014–2015. Beijing, Shanghai, and Guangzhou had suffered more severe O3 pollution, yet Beijing had the highest number of days that exceeded the Chinese MDA8 (maximum daily 8 h average) standard of 160 µg m−3. MDA8 O3 exhibited different seasonal patterns among the five cities. In Beijing and Xi’an, MDA8 O3 showed the highest in summer and lowest in winter. Guangzhou also had the highest O3 concentration in summer, but had similar levels in other three seasons. The O3 levels were similarly high in Shanghai during spring, summer, and autumn, while in Hefei, O3 concentration peaked in autumn. No significant difference between weekend and weekday O3 levels was observed in all the five cities. The diurnal cycle reached a maximum in the afternoon and a minimum in the early morning, which was consistent in the five cities. Correlation analyses showed that the associations between O3 and the other five criteria air pollutants, as well as meteorological parameters, were substantially different among the five cities. Air mass cluster analyses during episodic days revealed that the short-distance transport of O3 and its precursors had a greater impact for high O3 pollution in the five cities. Overall, our results demonstrate that O3 pollution exhibited great divergence among different regions and thus region-oriented control measures are suggested to reduce O3 pollution in China.

Evaluate Air Pollution by Promethee Ranking in Yangtze River Delta of China

A series of problems that are related to population, resources, environment, and ecology have emerged in recent years with the advancement of industrialization and urbanization in China. Especially, air pollution has become a severe trouble that directly endangers the health of residents. Accordingly, it is a need to make the assessment of air quality among cities, so that corresponding measures can be taken. For this purpose, ten major cities are selected as the research objects in Yangtze River Delta. Additionally, this study gathers and processes the data of five main air pollutants PM_2.5, PM₁₀, SO₂, O₃, and NO₂, respectively. Furthermore, the maximizing deviation method is used to obtain the respective weight of these pollutants and the preference ranking organization method for enrichment evaluations (PROMETHEE) is introduced into the assessment of air quality among ten cities. As a result, the ranking of air quality in Ningbo, Wenzhou, Shanghai, and Shaoxing was at the fore from 2014 to 2017. Meanwhile, the performance of Ningbo has always kept the top two and Shaoxing’s ranking has risen since 2015. In addition, the air quality of Changzhou, Suzhou and Hangzhou was at an average level in the past four years. Moreover, the performance of Nanjing, Wuxi, and Zhenjiang was terrible when compared to other cities. Some useful suggestions have been proposed to control air quality based on the ranking results.

Independent and Combined Effects of Heatwaves and PM2.5 on Preterm Birth in Guangzhou, China: A Survival Analysis

BACKGROUND

Both extreme heat and air pollution exposure during pregnancy have been associated with preterm birth; however, their combined effects are unclear.

OBJECTIVES

Our goal was to estimate the independent and joint effects of heatwaves and fine particulate matter [PM <2.5μm in aerodynamic diameter (PM2.5)], exposure during the final gestational week on preterm birth.

METHODS

Using birth registry data from Guangzhou, China, we included 215,059 singleton live births in the warm season (1 May-31 October) between January 2015 and July 2017. Daily meteorological variables from 5 monitoring stations and PM2.5 concentrations from 11 sites were used to estimate district-specific exposures. A series of cut off temperature thresholds and durations (2, 3, and 4 consecutive d) were used to define 15 different heatwaves. Cox proportional hazard models were used to estimate the effects of heatwaves and PM2.5 exposures during the final week on preterm birth, and departures from additive joint effects were assessed using the relative excess risk due to interaction (RERI).

RESULTS

Numbers of preterm births increased in association with heatwave exposures during the final gestational week. Depending on the heatwave definition used, hazard ratios (HRs) ranged from 1.10 (95% CI: 1.01, 1.20) to 1.92 (1.39, 2.64). Associations were stronger for more intense heatwaves. Combined effects of PM2.5 exposures and heatwaves appeared to be synergistic (RERIs>0) for less extreme heatwaves (i.e., shorter or with relatively low temperature thresholds) but were less than additive (RERIs<0) for more intense heatwaves.

CONCLUSIONS

Our research strengthens the evidence that exposure to heatwaves during the final gestational week can independently trigger preterm birth. Moderate heatwaves may also act synergistically with PM2.5 exposure to increase risk of preterm birth, which adds new evidence to the current understanding of combined effects of air pollution and meteorological variables on adverse birth outcomes. https://doi.org/10.1289/EHP5117.

Spatial Econometric Analysis of the Impact of Socioeconomic Factors on PM2.5 Concentration in China's Inland Cities: A Case Study from Chengdu Plain Economic Zone

Particulate matter with a diameter less than 2.5 µm (PM_2.5), one of the main sources of air pollution, has increasingly become a concern of the people and governments in China. Examining the socioeconomic factors influencing on PM_2.5 concentration is important for regional prevention and control. Previous studies mainly concentrated on the economically developed eastern coastal cities, but few studies focused on inland cities. This study selected Chengdu Plain Economic Zone (CPEZ), an inland region with heavy smog, and used spatial econometrics methods to identify the spatiotemporal distribution characteristics of PM_2.5 concentration and the socioeconomic factors underlying it from 2006 to 2016. Moran’s index indicates that PM_2.5 concentration in CPEZ does have spatial aggregation characteristics. In general, the spatial clustering from the fluctuation state to the stable low state decreased by 1% annually on average, from 0.190 (p < 0.05) in 2006 to 0.083 (p < 0.1) in 2016. According to the results of the spatial Durbin model (SDM), socioeconomic factors including population density, energy consumption per unit of output, gross domestic product (GDP), and per capita GDP have a positive effect on PM_2.5 concentration, while greening rate and per capita park space have a negative effect. Additionally, those factors have identified spatial spillover effects on PM_2.5 concentration. This study could be a reference and support for the formulation of more efficient air pollution control policies in inland cities.

Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities

OBJECTIVE

Ambient particulate pollution, especially PM_2.5, has adverse impacts on health and welfare. To manage and control PM_2.5 pollution, it is of great importance to determine the factors that affect PM_2.5 levels. Previous studies commonly focused on a single or several cities. This study aims to analyze the impacts of meteorological and socio-economic factors on daily concentrations of PM_2.5 in 109 Chinese cities from January 1, 2015 to December 31, 2015.

METHODS

To evaluate potential risk factors associated with the spatial and temporal variations in PM_2.5 levels, we developed a Bayesian spatio-temporal model in which the potential temporal autocorrelation and spatial autocorrelation of PM_2.5 levels were taken into account to ensure the independence of the error term of the model and hence the robustness of the estimated parameters.

RESULTS

Daily concentrations of PM_2.5 peaked in winter and troughed in summer. The annual average concentration reached its highest value (79 μg/m³) in the Beijing-Tianjin-Hebei area. The city-level PM_2.5 was positively associated with the proportion of the secondary industry, the total consumption of liquefied petroleum gas and the total emissions of industrial sulfur dioxide (SO₂), but negatively associated with the proportion of the primary industry. A reverse U-shaped relationship between population density and PM_2.5 was found. The city-level and daily-level of weather conditions within a city were both associated with PM_2.5.

CONCLUSION

PM_2.5 levels had significant spatio-temporal variations which were associated with socioeconomic and meteorological factors. Particularly, economic structure was a determinant factor of PM_2.5 pollution rather than per capita GDP. This finding will be helpful for the intervention planning of particulate pollution control when considering the environmental and social-economic factors as part of the strategies.

How to Maintain a Sustainable Environment? A Spatial Evolution of Urban Atmospheric Pollution and Impact Factors in China

Urban pollution has significantly contributed to the spread of diseases and global warming. The analysis of spatial distribution characteristics of atmospheric pollutants is crucial for making sustainable industrial policy, and environmentally friendly urban planning. In this paper, GeoDa software is used to analyze how sulfur dioxide (SO2), nitrogen oxides (NOx), and smoke dust (DUS) are spatially distributed in various provinces of China. Then, global spatial correlation test and cluster analysis are carried out to obtain the spatial evolution characteristics of three pollutants. Afterward, the spatial panel data model is applied to explore the factors that affect the spatial evolution of SO2, NOx and smoke dust (DUS) nationwide. MATLAB is used to estimate the Spatial Lag Model (SLM) and the Spatial Error Model (SEM) of the three pollutants, respectively. According to our analysis, SEM is more applicable for SO2 and NOx, whereas SLM is optimal for smoke dust (DUS). The results show that foreign direct investment (FDI), industrial structure, and urbanization aggravate environmental pollution, while per capita gross domestic products (per capita GDP) has a negative relationship with the cluster of pollutants. The study concludes by informing public policy makers on environment friendly policies for a more sustainable development.

Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China

The air quality over China exhibits seasonal and regional variation, resulting from heterogeneity in industrialization, and is highly affected by variability in meteorological conditions. We performed the first national-scale exploration of the relationship between the Air Pollution Index (API) and multiple meteorological parameters in China, using partial correlation and hierarchical cluster analyses. Relative humidity, wind speed, and temperature were the dominant factors influencing air quality year-round, due to their significant effects on pollutant dispersion and/or transformation of pollutants. The response of the API to single or multiple meteorological factors varied among cities and seasons, and a regional clustering of response mechanisms was observed, particularly in winter. Clear north–south differentiation was detected in the mechanisms of API response to relative humidity and wind speed. These findings provide insight into the spatiotemporal variation in air quality sensitivity to meteorological conditions, which will be useful for implementing regional air pollution control strategies.

Response of Global Air Pollutant Emissions to Climate Change and Its Potential Effects on Human Life Expectancy Loss

Geographical environment and climate change are basic factors for spatial fluctuations in the global distribution of air pollutants. Against the background of global climate change, further investigation is needed on how meteorological characteristics and complex geographical environment variations can drive spatial air pollution variations. This study analyzed the response of air pollutant emissions to climate change and the potential effects of air pollutant emissions on human health by integrating the air pollutant emission simulation model (GAINS) with 3 versions and CMIP5. The mechanism by which meteorological characteristics and geographical matrices can drive air pollution based on monitoring data at the site-scale was also examined. We found the total global emission of major air pollutants increased 1.32 times during 1970–2010. Air pollutant emissions will increase 2.89% and 4.11% in China and developed countries when the scenario of only maximum technically feasible reductions is performed (V4a) during 2020–2050. However, it will decrease 19.33% and 6.78% respectively by taking the V5a climate scenario into consideration, and precipitation variation will contribute more to such change, especially in China. Locally, the air circulation mode that is dominated by local geographical matrices and meteorological characteristics jointly affect the dilution and diffusion of air pollutants. Therefore, natural conditions, such as climate changes, meteorological characteristics and topography, play an important role in spatial air pollutant emissions and fluctuations, and must be given more attention in the processes of air pollution control policy making.

Introducing of modeling techniques in the research of POPs in breast milk - A pilot study

This study used advanced statistical and machine learning methods to investigate organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in breast milk, assuming that in a complex biological mixture, the pollutants emitted from the same source or with similar properties are statistically interrelated and possibly exhibit non-linear dynamics. The elaborated analyses such as Unmix source apportionment characterized individual source groups, while guided regularized random forest indicated the pollutant dependence on the ortho-chlorine atom attached to the congener's phenyl ring and mother's age. Mutual associations among PCBs were further discussed, but the results implied they were mostly not related to child delivery. PCB congeners -153, -180, -170, -118, -156, -105, and -138 appeared to be compounds of the outmost importance for mutual prediction with reference to their interrelations regarding chemical structure and metabolic processes in the mother's body. Finally, machine learning methods, which provided prediction relative errors lower than 30% and correlation coefficients higher than 0.90, suggested a possible strong non-linear relationship among the pollutants and consequently, the complexity of their pathways in the breast milk.

Examining the spatially varying effects of factors on PM2.5 concentrations in Chinese cities using geographically weighted regression modeling

Whilst numerous studies have explored the spatial patterns and underlying causes of PM_2.5, little attention has been paid to the spatial heterogeneity of the factors affecting PM_2.5. In this study, a geographically weighted regression (GWR) model was used to explore the strength and direction of nexus between various factors and PM_2.5 in Chinese cities. A comprehensive interpretive framework was established, composed of 18 determinants spanning the three categories of natural conditions, socioeconomic factors, and city features. Our results indicate that PM_2.5 concentration levels were spatially heterogeneous and markedly higher in cities in eastern China than in cities in the west of the country. Based on the results of GWR, significant spatial heterogeneity was identified in both the direction and strength of the determinants at the local scale. Among all of the natural variables, elevation was found to be statistically significant with its effects on PM_2.5 in 95.60% of the cities and it correlated negatively with PM_2.5 in 99.63% cities, with its effect gradually weakening from the eastern to the western parts of China. The variable of built-up areas emerged as the strongest variable amongst the socioeconomic variables studied; it maintained a positive significant relationship in cities located in the Pearl River Delta and surrounding areas, while in other cities it exhibited a negative relationship to PM_2.5. The highest coefficients were located in cities in northeast China. As the strongest variable amongst the six landscape factors, patch density maintained a positive relationship in part of cities. While in cities in the northeast regions, patch density exhibited a negative relationship with PM_2.5, revealing that increasing urban fragmentation was conducive to PM_2.5 reductions in those regions. These empirical results provide a basis for the formulation of targeted and differentiated air quality improvement measures in the task of regional PM_2.5 governances.

Fine-scale spatial structure of air pollutant concentrations along bus routes

Air pollution measurements were made at 5 s intervals along a bus route in Hong Kong, chosen to avoid frequent intersections or traffic jams. This allowed periodic pollutant concentration patterns in bus-derived pollutants to be explored using Fourier analysis. The analysis showed the defining role of the bus stops in terms of NO_X and PM_2.5 concentrations, which have profiles with a sawtooth form. Early in inter-stop segments, concentrations are high, followed by a decline. This arises from high emissions during the acceleration away from the bus stop, then lower emissions during cruise and de-acceleration. This pattern can be enhanced on sections of the route where buses are frequent as pollutants accumulate from the larger number of buses. The high concentrations just beyond bus stops may have implications for planning bus routes and the positioning of bus stops.

Assessment of MERRA-2 Surface PM2.5 over the Yangtze River Basin: Ground-based Verification, Spatiotemporal Distribution and Meteorological Dependence

A good understanding of how meteorological conditions exacerbate or mitigate air pollution is critical for developing robust emission reduction policies. Thus, based on a multiple linear regression (MLR) model in this study, the quantified impacts of six meteorological variables on PM2.5 (i.e., particle matter with diameter of 2.5 µm or less) and its major components were estimated over the Yangtze River Basin (YRB). The 38-year (1980–2017) daily PM2.5 and meteorological data were derived from the newly-released Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2) products. The MERRA-2 PM2.5 was underestimated compared with ground measurements, partly due to the bias in the MERRA-2 Aerosol Optical Depth (AOD) assimilation. An over-increasing trend in each PM2.5 component occurred for the whole study period; however, this has been curbed since 2007. The MLR model suggested that meteorological variability could explain up to 67% of the PM2.5 changes. PM2.5 was robustly anti-correlated with surface wind speed, precipitation and boundary layer height (BLH), but was positively correlated with temperature throughout the YRB. The relationship of relative humidity (RH) and total cloud cover with PM2.5 showed regional dependencies, with negative correlation in the Yangtze River Delta (YRD) and positive correlation in the other areas. In particular, PM2.5 was most sensitive to surface wind speed, and the sensitivity was approximately −2.42 µg m−3 m−1 s. This study highlighted the impact of meteorological conditions on PM2.5 growth, although it was much smaller than the anthropogenic emissions impact.

PM2.5 Spatiotemporal Evolution and Drivers in the Yangtze River Delta between 2005 and 2015

High concentrations of PM2.5 are a primary cause of haze in the lower atmosphere. A better understanding of the spatial heterogeneity and driving factors of PM2.5 concentrations is important for effective regional prevention and control. In this study, we carried out remote sensing inversion of PM2.5 concentration data over a long time series and used spatial statistical analyses and a geographical detector model to reveal the spatial distribution and variation characteristics of PM2.5 and the main influencing factors in the Yangtze River Delta from 2005 to 2015. Our results show that (1) The average annual PM2.5 concentration in the Yangtze River Delta prior to 2007 displayed an increasing trend, followed by a decreasing trend after 2007 which eventually stabilized; and (2) climate regionalization and geomorphology were the dominant natural factors driving PM2.5 concentration diffusion, while total carbon dioxide emissions and population density were the dominant socioeconomic factors affecting the formation of PM2.5. Natural factors and socioeconomic factors together lead to PM2.5 pollution. These findings provide an interpretation of PM2.5 spatial distribution and the mechanisms influencing PM2.5 pollution, which can help the Chinese government develop effective abatement strategies.

Air pollution characteristics in China during 2015-2016: Spatiotemporal variations and key meteorological factors

With rapid economic development and urbanization, China has suffered from severe and persistent air pollution during the past years. In the work, the hourly data of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ in all of the prefecture-level cities (336 cities) during 2015-2016 were collected to uncover the spatiotemporal variations and influential factors of these pollutants in China. The average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO decreased by 19.32%, 15.34%, 29.30%, 9.39%, and 8.00% from 2015 to 2016, suggesting the effects of efficient control measurements during this period. On the contrary, the O₃ concentration increased by 4.20% during the same period, which mainly owed to high volatile organic compounds (VOCs) loading. The concentrations of PM_2.5, PM₁₀, SO₂, CO and NO₂ showed the highest and the lowest ones in winter and summer, respectively. However, the O₃ concentration peaked in summer, followed by ones in spring and autumn, and presented the lowest one in winter. All of the pollutants exhibited significantly weekly and diurnal cycle in China. PM_2.5, PM₁₀, SO₂, CO and NO₂ presented the higher concentrations on weekdays than those at weekends, all of which showed the bimodal pattern with two peaks at late night (21:00-22:00) and in morning (9:00-10:00), respectively. However, the O₃ concentration exhibited the highest value around 15:00. The statistical analysis suggested that the PM_2.5, PM₁₀, and SO₂ concentrations were significantly associated with precipitation (Prec), atmosphere temperature (T), and wind speed (WS). The CO and NO₂ concentrations displayed the significant relationship with T, while the O₃ concentration was closely linked to the sunshine duration (Tsun) and relative humidity (RH). T and WS were major factors affecting the accumulation of PM and gaseous pollutants at a national scale. At a spatial scale, Prec and T played the important roles on the PM distribution in Northeast China, and the effect of Prec on CO concentration decreased from Southeast China to Northwest China. The results shown herein provide a scientific insight into the meteorology impacts on air pollution over China.

The burden associated with ambient PM2.5 and meteorological factors in Guangzhou, China, 2012-2016: A generalized additive modeling of temporal years of life lost

BACKGROUND

Daily exposure to ambient particulate matter with aerodynamic diameter <2.5 μm (PM_2.5) increases deaths and is an important contributor to burden of disease in population. To better understand the disease burden associated with PM_2.5, we examined the effects of PM_2.5 on daily years of life lost (YLL) in Guangzhou, China.

METHODS

Using Guangzhou death registry, air pollution and meteorological database, we applied generalized additive models (GAM) to the relationships between YLL and PM_2.5. We then adjusted the models for age, gender, seasonality and meteorological variables. We also conducted within-data prediction of YLL while setting 2012-2014 as baseline.

RESULTS

Over 2 million YLLs (800,137 YLLs for females and 1,212,040 YLLs for males) were observed during 2012-2016. YLL was higher for the elderly people. Mean daily average PM_2.5 concentration was 47.3 μg/m³. In model comparisons, the GAM with six meteorological variables (sunshine hours, relative humidity, precipitation, atmospheric pressure, wind speed, evaporation) outperformed the others. The R² and total deviance were 0.542 and 53.0%, respectively. Non-linear trends were observed for PM_2.5 and meteorological variables. Fitted daily YLL increased to the highest level, when PM_2.5 concentration reached 134.3 μg/m³ and atmospheric pressure reached 99.4 kPa. Within-data prediction supported the fitted GAM, where low mean absolute percentage errors were observed.

CONCLUSIONS

Daily PM_2.5 exposure has a nonlinear effect on YLL and increased levels of PM_2.5 may lead to increased YLL. This study highlights the urge to reduce ambient PM_2.5 pollution in Guangzhou, in order to promote environmental health.

Analysis of Spatial-Temporal Characteristics of the PM2.5 Concentrations in Weifang City, China

Air pollution, which accompanies industrial progression and urbanization, has become an urgent issue to address in contemporary society. As a result, our understanding and continued study of the spatial-temporal characteristics of a major pollutant, defined as 2.5-micron or less particulate matter (PM2.5), as well as the development of related approaches to improve the environment, has become vital. This paper studies the characteristics of yearly, quarterly, monthly, daily, and hourly PM2.5 concentrations, and discusses the influencing factors based on the hourly data of nationally controlled and provincially controlled monitoring stations, from 2012 to 2016, in Weifang City. The main conclusion of this study is that the annual PM2.5 concentrations reached a peak in 2013. With efficient aid from the government, this value has decreased annually and has high spatial characteristics in the northwest and low spatial characteristics in the southeast. Second, the seasonal and monthly PM2.5 concentrations form a U-shaped trend, meaning that the concentration is high in the summer and low in the winter. These trends are highly relevant to the factors of plantation, humidity, temperature, and precipitation. Third, within a week, higher PM2.5 concentrations appear on Mondays and Saturdays, whereas the lowest concentration occurs on Wednesdays. It can be inferred that PM2.5 concentrations tend to be highly dependent on human activities and living habits. Lastly, there are hourly discrepancies within the peaks and troughs depending on the month, and the overall daytime PM2.5 concentrations and reductive rates are higher in the daytime than in the nighttime.

Examining the Impacts of Urban Form on Air Pollution in Developing Countries: A Case Study of China's Megacities

Urban form is increasingly being identified as an important determinant of air pollution in developed countries. However, the effect of urban form on air pollution in developing countries has not been adequately addressed in the literature to date, which points to an evident omission in current literature. In order to fill this gap, this study was designed to estimate the impacts of urban form on air pollution for a panel made up of China's five most rapidly developing megacities (Beijing, Tianjin, Shanghai, Chongqing, and Guangzhou) using time series data from 2000 to 2012. Using the official Air Pollution Index (API) data, this study developed three quantitative indicators: mean air pollution index (MAPI), air pollution ratio (APR), and continuous air pollution ratio (CAPR), to evaluate air pollution levels. Moreover, seven landscape metrics were calculated for the assessment of urban form based on three aspects (urban size, urban shape irregularity, and urban fragmentation) using remote sensing data. Panel data models were subsequently employed to quantify the links between urban form and air pollution. The empirical results demonstrate that urban expansion surprisingly helps to reduce air pollution. The substitution of clean energy for dirty energy that results from urbanization in China offers a possible explanation for this finding. Furthermore, urban shape irregularity positively correlated with the number of days with polluted air conditions, a result could be explained in terms of the influence of urban geometry on traffic congestion in Chinese cities. In addition, a negative association was identified between urban fragmentation and the number of continuous days of air pollution, indicating that polycentric urban forms should be adopted in order to shorten continuous pollution processes. If serious about achieving the meaningful alleviation of air pollution, decision makers and urban planners should take urban form into account when developing sustainable cities in developing countries like China.

Effects of prenatal exposure to air pollution on preeclampsia in Shenzhen, China

The impact of ambient air pollution on pregnant women is a concern in China. However, little is known about the association between air pollution and preeclampsia and the potential modifying effects of meteorological conditions have not been assessed. This study aimed to assess the effects of prenatal exposure to air pollution on preeclampsia, and to explore whether temperature and humidity modify the effects. We performed a retrospective cohort study based on 1.21 million singleton births from the birth registration system in Shenzhen, China, between 2005 and 2012. Daily average measurements of particulate matter <10 μm (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), air temperature (T), and dew point (T_d) were collected. Logistic regression models were performed to estimate associations between air pollution and preeclampsia during the first and second trimesters, and during the entire pregnancy. In each time window, we observed a positive gradient of increasing preeclampsia risk with increasing quartiles of PM₁₀ and SO₂ exposure. When stratified by T and T_d in three categories (<5th, 5th -95th, and >95th percentile), we found a significant interaction between PM₁₀ and T_d on preeclampsia; the adverse effects of PM₁₀ increased with T_d. During the entire pregnancy, there was a null association between PM₁₀ and preeclampsia under T_d < 5th percentile. Preeclampsia risk increased by 23% (95% CI: 19-26%) when 5th < T_d < 95th percentile, and by 34% (16-55%) when T_d > 95th percentile. We also found that air pollution effects on preeclampsia in autumn/winter seasons were stronger than those in the spring/summer. This is the first study to address modifying effects of meteorological factors on the association between air pollution and preeclampsia. Findings indicate that prenatal exposure to PM₁₀ and SO₂ increase preeclampsia risk in Shenzhen, China, and the effects could be modified by humidity. Pregnant women should limit air pollution exposure, particularly during humid periods.

Health status and air pollution related socioeconomic concerns in urban China

Background

China is experiencing environmental issues and related health effects due to its industrialization and urbanization. The health effects associated with air pollution are not just a matter of epidemiology and environmental science research, but also an important social science issue. Literature about the relationship of socioeconomic factors with the environment and health factors is inadequate. The relationship between air pollution exposure and health effects in China was investigated with consideration of the socioeconomic factors.

Methods

Based on nationwide survey data of China in 2014, we applied the multilevel mixed-effects model to evaluate how socioeconomic status (represented by education and income) contributed to the relationship between self-rated air pollution and self-rated health status at community level and individual level.

Results

The findings indicated that there was a non-linear relationship between the community socioeconomic status and community air pollution in urban China, with the highest level of air pollution presented in the communities with moderate socioeconomic status. In addition, health effects associated air pollution in different socioeconomic status groups were not equal. Self-rated air pollution had the greatest impact on self-rated health of the lower socioeconomic groups. With the increase of socioeconomic status, the effect of self-rated air pollution on self-rated health decreased.

Conclusions

This study verified the different levels of exposure to air pollution and inequality in health effects among different socioeconomic groups in China. It is imperative for the government to urgently formulate public policies to enhance the ability of the lower socioeconomic groups to circumvent air pollution and reduce the health damage caused by air pollution.

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration of contaminants. It may also lead to the identification of hotspots. The patterns can then be targeted to manage the concentration level of pollutants. In this regard, employing spatial autocorrelation indices as important tools is inevitable. In this study, general and local indices of Moran’s I and Getis-Ord statistics were assessed in their representation of the structural characteristics of carbon monoxide (CO) and fine particulate matter (PM2.5) polluted areas in Tehran, Iran, which is one of the most polluted cities in the world. For this purpose, a grid (200 m × 200 m) was applied across the city, and the inverse distance weighted (IDW) interpolation method was used to allocate a value to each pixel. To compare the methods of detecting clusters meaningfully and quantitatively, the pollution cleanliness index (PCI) was established. The results ascertained a high clustering level of the pollutants in the study area (with 99% confidence level). PM2.5 clusters separated the city into northern and southern parts, as most of the cold spots were situated in the north half and the hotspots were in the south. However, the CO hotspots also covered an area from the northeast to southwest of the city and the cold spots were spread over the rest of the city. The Getis-Ord’s PCI suggested a more polluted air quality than the Moran’s I PCI. The study provides a feasible methodology for urban planners and decision makers to effectively investigate and govern contaminated sites with the aim of reducing the harmful effects of air pollution on public health and the environment.

Temporal and spatial correlation patterns of air pollutants in Chinese cities

As a huge threat to the public health, China’s air pollution has attracted extensive attention and continues to grow in tandem with the economy. Although the real-time air quality report can be utilized to update our knowledge on air quality, questions about how pollutants evolve across time and how pollutants are spatially correlated still remain a puzzle. In view of this point, we adopt the PMFG network method to analyze the six pollutants’ hourly data in 350 Chinese cities in an attempt to find out how these pollutants are correlated temporally and spatially. In terms of time dimension, the results indicate that, except for O₃, the pollutants have a common feature of the strong intraday patterns of which the daily variations are composed of two contraction periods and two expansion periods. Besides, all the time series of the six pollutants possess strong long-term correlations, and this temporal memory effect helps to explain why smoggy days are always followed by one after another. In terms of space dimension, the correlation structure shows that O₃ is characterized by the highest spatial connections. The PMFGs reveal the relationship between this spatial correlation and provincial administrative divisions by filtering the hierarchical structure in the correlation matrix and refining the cliques as the tinny spatial clusters. Finally, we check the stability of the correlation structure and conclude that, except for PM₁₀ and O₃, the other pollutants have an overall stable correlation, and all pollutants have a slight trend to become more divergent in space. These results not only enhance our understanding of the air pollutants’ evolutionary process, but also shed lights on the application of complex network methods into geographic issues.

Water quality trend and change-point analyses using integration of locally weighted polynomial regression and segmented regression

Trend and change-point analyses of water quality time series data have important implications for pollution control and environmental decision-making. This paper developed a new approach to assess trends and change-points of water quality parameters by integrating locally weighted polynomial regression (LWPR) and segmented regression (SegReg). Firstly, LWPR was used to pretreat the original water quality data into a smoothed time series to represent the long-term trend of water quality. Then, SegReg was used to identify the long-term trends and change-points of the smoothed time series. Finally, statistical tests were applied to determine the significance of the long-term trends and change-points. The efficacy of this approach was validated using a 10-year record of total nitrogen (TN) and chemical oxygen demand (COD_Mn) from Shanxi Reservoir watershed in eastern China. Results showed that this approach was straightforward and reliable for assessment of long-term trends and change-points on irregular water quality datasets. The reliability was verified by statistical tests and practical considerations for Shanxi Reservoir watershed. The newly developed integrated LWPR-SegReg approach is not only limited to the assessment of trends and change-points of water quality parameters but also has a broad application to other fields with long-term time series records.

Effect of short-term regional traffic restriction on urban submicron particulate pollution

During the 2013 and 2015 Lanzhou International Marathon Events (LIME1 and LIME2), the local government made a significant effort to improve traffic conditions and air quality by implementing traffic restriction measures. To fill the gap in information on the effect of short-period (several hours) traffic control on urban air quality, submicron particle size distributions and meteorological data were measured simultaneously during June 2013 and June 2015 in urban Lanzhou. The number and surface area concentrations of particles in the 100-200nm range declined by 67.2% and 65.0% for LIME1 due to traffic control, while they decreased by 39.2% and 37.1% for LIME2. The impact of traffic restriction on air pollution near the sampling site lagged behind the traffic control period for LIME2. In addition, the effect of traffic restriction on air pollution near the sampling site was dependent on the distance between the relative orientation of the sampling site and traffic-restricted zones, as well as meteorological conditions such as wind direction. The influence of traffic restrictions on the particle concentrations differed for different particle sizes. The size range most affected by traffic restriction was 60-200 and 60-300nm for number and surface area concentrations in the urban environment, respectively, while for the particle volume concentration it was the 100-600nm range. This study will provide a basis for implementation of future urban traffic-induced particulate pollution control measures.

Spatial and Temporal Distributions of Air Pollutants and Size Distribution of Aerosols over Central and Eastern China

The origins and spatial and temporal distributions of air pollutants (PM_2.5, PM₁₀, CO, SO₂, NO₂ and O₃) during May to June of 2015 were investigated using data from 1490 monitoring sites in China. Aerosol number concentrations and meteorological data from Shijiazhuang, Nanjing, and Suzhou were combined with the MIX Asian emission data and the HYSPLIT model. Furthermore, the diurnal variation, size distribution, and main sources of air pollutants and aerosols were selectively characterized in the North China Plain (NCP) and the Yangtze River Delta (YRD). High values of particulate matter concentrations (PM), including PM_2.5 and PM₁₀, occurred in the northwestern and central regions of eastern China. Elevated PM_2.5 and PM₁₀ concentrations represented natural dust sources and anthropogenic resident, power plant, industry, and traffic emissions sources, respectively. The concentrated distributions of SO₂ were similar to those of PM. The CO concentrations were distributed uniformly in China. High O₃ values occurred above the Qinghai province. During the observation period, the air masses mainly originated from the northwest NCP and from the southwest or northeastern ocean in the YRD, resulting in high concentrations of PM_2.5, PM₁₀, SO₂, and CO in the NCP, the average values of which were 61.8 ± 40.0, 118.8 ± 66.4, 24.1 ± 24.6 μg m^-3, and 1.2 ± 0.9 mg m^-3, respectively, and were 1.2, 1.4, 1.5, and 1.3 times larger than those in the YRD. NO₂ had higher concentrations in the YRD with an average of 43.7 ± 24.8 μg m^-3, which was 1.2 times larger than that in the NCP. The diurnal variations of PM, NO₂ and CO had bimodal distributions and SO₂ and O₃ had unimodal distributions in the NCP and YRD. The aerosol number concentrations had average values of 12,661 ± 5266, 11,189 ± 5905, and 12,797 ± 5931 cm^-3 in Shijiazhuang, Nanjing, and Suzhou. Their diurnal variations displayed trimodal peaks at 18:00-21:00, 11:00-14:00, and 06:00-08:00, and their spectra distributions were all unimodal with peaks at 60-70, 60-70, and 100-110 nm, respectively.

The impact of anthropogenic emissions and meteorological conditions on the spatial variation of ambient SO2 concentrations: A panel study of 113 Chinese cities

China has received increased international criticism in recent years in relation to its air pollution levels, both in terms of the transmission of pollutants across international borders and the attendant adverse health effects being witnessed. Whilst existing research has examined the factors influencing ambient air pollutant concentrations, previous studies have failed to adequately explore the determinants of such concentrations from either a source or diffusion perspective. This study addressed both source (specifically, anthropogenic emissions) and diffusion (namely, meteorological conditions) indicators, in order to detect their respective impacts on the spatial variations seen in the distribution of air pollution. Spatial panel data for 113 major cities in China was processed using a range of global regression models-the ordinary least square model, the spatial lag model, and the spatial error model-as well as a local, geographic weighted regression (GWR) model. Results from the study suggest that in 2014, average SO₂ concentrations exceeded China's first-level target. The most polluted cities were found to be predominantly located in northern China, while less polluted cities were located in southern China. Global regression results indicated that precipitation exerts a significant effect on SO₂ reduction (p<0.001) and that a regional increase of 1mm in precipitation can reduce SO₂ concentrations by 0.026μg/m³. Both emission and temperature factors were found to aggravate SO₂ concentrations, although no such significant correlation was found in relation to wind speed. GWR results suggest that the association between SO₂ and its factors varied over space. Increased emissions were found to be able to produce more pollution in the northwest than in other parts of the country. Higher wind speeds and temperatures in northwestern areas were shown to reinforce SO₂ pollution, while in southern regions, they had the opposite effect. Further, increased precipitation was found to exert a greater inhibitory effect on SO₂ pollution in the country's northeast than that in other areas. Our findings could provide a detailed reference for formulating regionally specific emission reduction policies in China.

Typical atmospheric haze during crop harvest season in northeastern China: A case in the Changchun region

This study presents the mass concentrations of PM_2.5, O₃, SO₂ and NO_x at one urban, one suburban and two rural locations in the Changchun region from September 25 to October 27 2013. Major chemical components of PM_2.5 at the four sites were daily sampled and analyzed. Most of daily concentrations of SO₂ (7-82μg/m³), O₃ (27-171μg/m³) and NO_x (14-213μg/m³) were below the limits of the National Ambient Air Quality Standard (NAAQS) in China. However, PM_2.5 concentrations (143-168μg/m³) were 2-fold higher than NAAQS_. Higher PM_2.5 concentrations (~150μg/m³) were measured during the pre-harvest and harvest at the urban site, while PM_2.5 concentrations significantly increased from 250 to 400μgm^-3 at suburban and rural sites with widespread biomass burning. At all sites, PM_2.5 components were dominated by organic carbon (OC) and followed by soluble component sulfate (SO₄^2-), ammonium (NH₄⁺) and nitrate (NO₃^-). Compared with rural sites, urban site had a higher mineral contribution and lower potassium (K⁺ and K) contribution to PM_2.5. Severe atmospheric haze events that occurred from October 21 to 23 were attributed to strong source emissions (e.g., biomass burning) and unfavorable air diffusion conditions. Furthermore, coal burning originating from winter heating supply beginning on October 18 increased the atmospheric pollutant emissions. For entire crop harvest period, the Positive Matrix Factorization (PMF) analysis indicated five important emission contributors in the Changchun region, as follows: secondary aerosol (39%), biomass burning (20%), supply heating (18%), soil/road dust (14%) and traffic (9%).

Seasonal patterns of dengue fever and associated climate factors in 4 provinces in Vietnam from 1994 to 2013

BACKGROUND

In Vietnam, dengue fever (DF) is still a leading cause of hospitalization. The main objective of this study was to evaluate the seasonality and association with climate factors (temperature and precipitation) on the incidences of DF in four provinces where the highest incidence rates were observed from 1994 to 2013 in Vietnam.

METHODS

Incidence rates (per 100,000) were calculated on a monthly basis from during the study period. The seasonal-decomposition procedure based on loess (STL) was used in order to assess the trend and seasonality of DF. In addition, a seasonal cycle subseries (SCS) plot and univariate negative binomial regression (NBR) model were used to evaluate the monthly variability with statistical analysis. Lastly, a generalized estimating equation (GEE) was used to assess the relationship between monthly incidence rates and weather factors (temperature and precipitation).

RESULTS

We found that increased incidence rates were observed in the second half of each year (from May through December) which is the rainy season in each province. In Hanoi, the final model showed that 1 °C rise of temperature corresponded to an increase of 13% in the monthly incidence rate of DF. In Khanh Hoa, the final model displayed that 1 °C increase in temperature corresponded to an increase of 17% while 100 mm increase in precipitation corresponded to an increase of 11% of DF incidence rate. For Ho Chi Minh City, none of variables were significant in the model. In An Giang, the final model showed that 100 mm increase of precipitation in the preceding and same months corresponded to an increase of 30% and 22% of DF incidence rate.

CONCLUSION

Our findings provide insight into understanding the seasonal pattern and associated climate risk factors.

Statistical analysis of Seoul air quality to assess the efficacy of emission abatement strategies since 1987

The combined influences of recent mitigation measures on urban air quality have been assessed using hourly observations of the criteria air pollutants (NO, NO₂, O₃, CO, and SO₂) made from the Yongsan district of Seoul, Korea, over 26years (1987 to 2013). A number of data selection criteria are proposed in order to minimize variability associated with temporal changes (at diurnal, weekly, and seasonal timescales) in source strengths, their spatial distribution, and the atmospheric volume into which they mix. The temporal constraints required to better characterize relationships between observed air quality and changes in source strengths in Seoul were identified as: (i) a 5-hour diurnal sampling window (1300-1700h), (b) weekday measurements (Monday to Friday only), and (c) summer measurements (when pollutant fetch is mostly Korea-specific, and mean wind speeds are the lowest). Using these selection criteria, we were able to closely relate long-term trends identified in criteria pollutants to a number of published changes to traffic-related source strengths brought about by mitigation measures adopted over the last 10-15years.

Spatial variation of air quality index and urban driving factors linkages: evidence from Chinese cities

Daily air quality index (AQI) of 161 Chinese cities obtained from the Ministry of Environmental Protection of China in 2015 is conducted. In this study, to better explore spatial distribution and regional characteristic of AQI, global and local spatial autocorrelation is utilized. Pearson's correlation is introduced to determine the influence of single urban indicator on AQI value. Meanwhile, multiple linear stepwise regression is chosen to estimate quantitatively the most influential urban indicators on AQI. The spatial autocorrelation analysis indicates that the AQI value of Chinese 161 cities shows a spatial dependency. Higher AQI is mainly located in north and northwest regions, whereas low AQI is concentrated in the south and the Qinghai-Tibet regions. The low AQI and high AQI values in China both exhibit relative immobility through seasonal variation. The influence degree of three adverse urban driving factors on AQI value is ranked from high to low: coal consumption of manufacturing > building area > coal consumption of the power industry. It is worth noting that the risk of exposed population to poor quality is greater in the northern region than in other regions. The results of the study provide a reference for the formulation of urban policy and improvement of air quality in China.

The covariance of air quality conditions in six cities in Southern Germany - The role of meteorology

This paper analyzed air quality in six cities in Southern Germany (Ulm, Augsburg, Konstanz, Freiburg, Stuttgart and Munich), in conjunction with the prevailing synoptic conditions. Air quality was estimated through the calculation of a daily Air Stress Index (ASI) constituted by five independent components, each one expressing the contribution of one of the five main pollutants (PM₁₀, O₃, SO₂, NO₂ and CO) to the total air stress. As it was deduced from ASI components, PM₁₀ from combustion sources and photochemically produced tropospheric O₃ are the most hazardous pollutants at the studied sites, throughout cold and warm periods respectively, yet PM₁₀ contribute substantially to the overall air stress during both seasons. The influence of anticyclonic high pressure systems, leading to atmospheric stagnation, was associated with increased ASI values, mainly due to the entrapment of PM₁₀. Moderate air stress was generally estimated in all cities however a cleaner atmosphere was detected principally in Freiburg when North Europe was dominated by low pressure systems. Daily events of notably escalated ASI values were further analyzed with backward air mass trajectories. Throughout cold period, ASI episodes were commonly related to eastern airflows carrying exogenous PM₁₀ originated from eastern continental Europe. During warm period, ASI episodes were connected to the arrival of regionally circulated air parcels reflecting lack of dispersion and accumulation of pollutants in accordance with the synoptic analysis.

Spatial and temporal trends in the mortality burden of air pollution in China: 2004-2012

While recent assessments have quantified the burden of air pollution at the national scale in China, air quality managers would benefit from assessments that disaggregate health impacts over regions and over time. We took advantage of a new 10×10km satellite-based PM_2.5 dataset to analyze spatial and temporal trends of air pollution health impacts in China, from 2004 to 2012. Results showed that national PM_2.5 related deaths from stroke, ischemic heart disease and lung cancer increased from approximately 800,000 cases in 2004 to over 1.2 million cases in 2012. The health burden exhibited strong spatial variations, with high attributable deaths concentrated in regions including the Beijing-Tianjin Metropolitan Region, Yangtze River Delta, Pearl River Delta, Sichuan Basin, Shandong, Wuhan Metropolitan Region, Changsha-Zhuzhou-Xiangtan, Henan, and Anhui, which have heavy air pollution, high population density, or both. Increasing trends were found in most provinces, but with varied growth rates. While there was some evidence for improving air quality in recent years, this was offset somewhat by the countervailing influences of in-migration together with population growth. We recommend that priority areas for future national air pollution control policies be adjusted to better reflect the spatial hotspots of health burdens. Satellite-based exposure and health impact assessments can be a useful tool for tracking progress on both air quality and population health burden reductions.

PM2.5 pollution is substantially affected by ammonia emissions in China

Urban air quality in China has been declining substantially in recent years due to severe haze episodes. The reduction of sulfur dioxide (SO₂) and nitrogen oxide (NO_x) emissions since 2013 does not yet appear to yield substantial benefits for haze mitigation. As the reductions of those key precursors to secondary aerosol formation appears not to sufficient, other crucial factors need to be considered for the design of effective air pollution control strategies. Here we argue that ammonia (NH₃) plays a - so far - underestimated role in the formation of secondary inorganic aerosols, a main component of urban fine particulate matter (PM_2.5) concentrations in China. By analyzing in situ concentration data observed in major cities alongside gridded emission data obtained from remote sensing and inventories, we find that emissions of NH₃ have a more robust association with the spatiotemporal variation of PM_2.5 levels than emissions of SO₂ and NO_x. As a consequence, we argue that urban PM_2.5 pollution in China in many locations is substantially affected by NH₃ emissions. We highlight that more efforts should be directed to the reduction of NH₃ emissions that help mitigate PM_2.5 pollution more efficiently than other PM_2.5 precursors. Such efforts will yield substantial co-benefits by improving nitrogen use efficiency in farming systems. As a consequence, such integrated strategies would not only improve urban air quality, but also contribute to China's food-security goals, prevent further biodiversity loss, reduce greenhouse gas emissions and lead to economic savings.

Impact of air pollution on the burden of chronic respiratory diseases in China: time for urgent action

In China, where air pollution has become a major threat to public health, public awareness of the detrimental effects of air pollution on respiratory health is increasing-particularly in relation to haze days. Air pollutant emission levels in China remain substantially higher than are those in developed countries. Moreover, industry, traffic, and household biomass combustion have become major sources of air pollutant emissions, with substantial spatial and temporal variations. In this Review, we focus on the major constituents of air pollutants and their impacts on chronic respiratory diseases. We highlight targets for interventions and recommendations for pollution reduction through industrial upgrading, vehicle and fuel renovation, improvements in public transportation, lowering of personal exposure, mitigation of the direct effects of air pollution through healthy city development, intervention at population-based level (systematic health education, intensive and individualised intervention, pre-emptive measures, and rehabilitation), and improvement in air quality. The implementation of a national environmental protection policy has become urgent.

A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day's Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3-7 days' AQI prediction.

The impact of ambient air pollution on suicide mortality: a case-crossover study in Guangzhou, China

BACKGROUND

Preventing suicide is a global imperative. Although the effects of social and individual risk factors of suicide have been widely investigated, evidence of environmental effects of exposure to air pollution is scarce. We investigated the effects of ambient air pollution on suicide mortality in Guangzhou, China during 2003-2012.

METHODS

A conditional logistic regression analysis with a time-stratified case-crossover design was performed to assess the effects of daily exposure to three standard air pollutants, including particulate matter less than 10 μm in aerodynamic diameter (PM10), sulphur dioxide (SO2) and nitrogen dioxide (NO2), on suicide mortality, after adjusting for the confounding effects of daily mean temperature, relative humidity, atmospheric pressure and sunshine duration. Further analyses were stratified by season, gender, age group, educational attainment and suicide type.

RESULTS

Between 2003 and 2012, there were a total of 1 550 registered suicide deaths in Guangzhou. A significant increase in suicide risk were associated with interquartile-range increases in the concentration of air pollutant, with an odds ratio of 1.13 (95 % confidence interval (CI): 1.01, 1.27) and 1.15 (95 % CI: 1.03, 1.28) for PM10 and NO2 at lag 02, and 1.12 (95 % CI: 1.02, 1.23) for SO2 at lag 01, respectively. The suicide risks related to air pollution for males and people with high education level were higher than for females and those with low education level, respectively. Significant air pollution effects were found on violent suicide mortality and in cool season but not on non-violent suicide mortality or in warm season.

CONCLUSIONS

Suicide risk was positively associated with ambient air pollution levels. This finding would provide important information for the health impact assessment of air pollution and for the development of effective strategies and interventions for the prevention of suicide.

Spatial-Temporal Analysis of Air Pollution, Climate Change, and Total Mortality in 120 Cities of China, 2012-2013

China has had a rapid increase in its economy over the past three decades. However, the economic boom came at a certain cost of depleting air quality. In the study, we aimed to examine the burden of air pollution and its association with climatic factors and health outcomes using data from Chinese national and city-level air quality and public health surveillance systems. City-level daily air pollution index (API, a sum weighted index of SO2, NO2, PM10, CO, and Ozone) in 120 cities in 2012 and 2013, and its association with climate factors were analyzed using multiple linear regression analysis, spatial autocorrelation analysis, and panel fixed models. City-level ecological association between annual average API and total mortality were examined using univariate and partial correlation analysis. Sensitivity analysis was conducted by taking the consideration of time-lag effect between exposures and outcomes. The results show that among the 120 cities, annual average API significantly increased from 2012 to 2013 (65.05 vs. 75.99, p < 0.0001). The highest average API was in winter, and the lowest in summer. A significantly spatial clustering of elevated API was observed, with the highest API in northwest China in 2012 and with the highest in east China in 2013. In 2012, 5 (4%) of the 120 cities had ≥60 days with API >100 (defined as "slightly polluted"), however, it increased to 21 cities (18%) that experienced API >100 for ≥60 days in 2013. Furthermore, 16 cities (13%) in 2012 and 35 (29%) in 2013 experienced a maximum API >300 (defined as "severely polluted"). API was negatively and significantly correlated with heat index, precipitation, and sunshine hours, but positively with air pressure. Cities with higher API concentrations had significantly higher total mortality rates than those with lower API. About a 4-7% of the variation in total mortality could be explained by the difference in API across the nation. In conclusion, the study highlights an increased trend of air pollution from 2012 to 2013 in China. The magnitude of air pollution varied by seasons and regions and correlated with climatic factors and total mortality across the country.

The burden of COPD mortality due to ambient air pollution in Guangzhou, China

Few studies have investigated the chronic obstructive pulmonary disease (COPD) mortality fraction attributable to air pollution and modification by individual characteristics of air pollution effects. We applied distributed lag non-linear models to assess the associations between air pollution and COPD mortality in 2007-2011 in Guangzhou, China, and the total COPD mortality fraction attributable to air pollution was calculated as well. We found that an increase of 10 μg/m(3) in particulate matter with an aerodynamic diameter of 10 μm or less (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) was associated with a 1.58% (95% confidence interval (CI): 0.12-3.06%), 3.45% (95% CI: 1.30-5.66%) and 2.35% (95% CI: 0.42-4.32%) increase of COPD mortality over a lag of 0-15 days, respectively. Greater air pollution effects were observed in the elderly, males and residents with low educational attainment. The results showed 10.91% (95% CI: 1.02-9.58%), 12.71% (95% CI: 5.03-19.85%) and 13.38% (95% CI: 2.67-22.84%) COPD mortality was attributable to current PM10, SO2 and NO2 exposure, respectively. In conclusion, the associations between air pollution and COPD mortality differed by individual characteristics. There were remarkable COPD mortality burdens attributable to air pollution in Guangzhou.

Characterization of haze episodes and factors contributing to their formation using a panel model

A haze episode is a complex pollution process with high levels of fine particulate matter smaller than 2.5 μm (PM2.5). Understanding factors contributing to their formation is crucial to mitigate PM2.5 pollution, which varies substantially on the daily and city scales. In this study, we attempted to introduce the dynamic panel model that uses the group deviation method to generate unbiased estimates of contributions from different factors by eliminating time-invariant confounding variables. Taking 25 cities in the Yangtze Delta Region (YDR), China, as a case study and we analyzed how natural factors (e.g., wind) and anthropogenic emissions (e.g., sulfur dioxide (SO2)) together contribute to PM2.5 pollution. Results showed that there was significant lag effect on PM2.5 concentration, and approximately 45% of the PM2.5 remained from one day to the next. On the contrary, present day's emission had little effect on its PM2.5 concentration. It suggested that daily variation of PM2.5 concentration was largely affected by natural factors, while the long term PM2.5 pollution such as annual concentration was more determined by anthropogenic emissions. The unbiased estimates of this simple dynamic panel model could well predict the annual changes of PM2.5 concentration with an uncertainty of less than 2% on city scale. Reducing SO2 and nitrogen oxide (NOx) emissions could mitigate PM2.5 pollution to some extent in the YDR; however, to achieve the clean air standard, more pollutants such as ammonia should be added to the emission reduction list. The analyses provide an alternative method to easily quantify contributing factors and their variability to air pollution. It could be helpful to better understand the confounding factors on the assessment of air pollution governance despite the panel model still need to be improved on aspects such as long-range transportation.