The air quality and health impacts of domestic trans-boundary pollution in various regions of China

Characterising personal, household, and community PM2.5 exposure in one urban and two rural communities in China

BACKGROUND

Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.

METHODS

We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May-September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326-80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.

FINDINGS

Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2-4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6-85.3] μg/m3 vs 41.6 [37.3-46.5] μg/m3), kitchen (102.4 [90.4-116.0] μg/m3 vs 52.3 [44.8-61.2] μg/m3) and living room (62.1 [57.3-67.3] μg/m3 vs 41.0 [37.1-45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700-1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64-0.66) and kitchen (0.52-0.59) levels, but only weakly correlated with community levels, especially in summer (0.15-0.34) and among solid fuel users (0.11-0.31).

CONCLUSION

Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5.

Multi-component emission characteristics and high-resolution emission inventory of non-road construction equipment (NRCE) in China

With the continuous abatement of industries and vehicles in the past years in China, the comprehensive understanding and scientific control of non-road construction equipment (NRCE) may play an important role in alleviating PM_2.5 and O₃ pollution in the next stage. In this study, the emission rates of CO, HC, NOx, PM_2.5, CO₂ and the component profiles of HC and PM_2.5 from 3 loaders, 8 excavators and 4 forklifts under different operating conditions were tested for a systematic representation of NRCE emission characteristics. With the fusion of field tests, construction land types and population distributions, the NRCE emission inventory with a 0.1° × 0.1° resolution in nationwide and with a 0.01° × 0.01° resolution in Beijing-Tianjin-Hebei region (BTH) were established. The sample testing results suggested prominent differences in instantaneous emission rates and the composition characteristics among different equipment and under different operating modes. Generally, for NRCE, the dominant components are OC and EC for PM_2.5, and HC and olefin for OVOC. Especially, the proportion of olefins in idling mode is much higher than that in working mode. The measurement-based emission factors of various equipment exceeded the Stage III standard to varying degrees. The high-resolution emission inventory suggested that highly developed central and eastern areas, represented by BTH, showed the most prominent emissions in China. This study is a systematic representations of China's NRCE emissions, and the NRCE emission inventory establishment method with multiple data fusion has important methodological reference value for other emission sources.

Sources, chemical components, and toxicological responses of size segregated urban air PM samples in high air pollution season in Guangzhou, China

The sources, sizes, components, and toxicological responses of particulate matter (PM) have demonstrated remarkable spatiotemporal variability. However, associations between components, sources, and toxicological effects in different-sized PM remain unclear. The purposes of this study were to 1) determine the sources of PM chemical components, 2) investigate the associations between components and toxicology of PM from Guangzhou high air pollution season. We collected size-segregated PM samples (PM_10-2.5, PM_2.5-1, PM_1-0.2, PM_0.2) from December 2017 to March 2018 in Guangzhou. PM sources and components were analyzed. RAW264.7 mouse macrophages were treated with PM samples for 24 h followed by measurements of toxicological responses. The concentrations of PM_10-2.5 and PM_1-0.2 were relatively high in all samples. Water-soluble ions and PAHs were more abundant in smaller-diameter PM, while metallic elements were more enriched in larger-diameter PM. Traffic exhaust, soil dust, and biomass burning/petrochemical were the most important sources of PAHs, metals and ions, respectively. The main contributions to PM were soil dust, coal combustion, and biomass burning/petrochemical. Exposure to PM_10-2.5 induced the most significant reduction of cell mitochondrial activity, oxidative stress and inflammatory response, whereas DNA damage, an increase of Sub G1/G0 population, and impaired cell membrane integrity were most evident with PM_1-0.2 exposure. There were moderate or strong correlations between most single chemicals and almost all toxicological endpoints as well as between various toxicological outcomes. Our findings highlight those various size-segregated PM-induced toxicological effects in cells, and identify chemical components and sources of PM that play the key role in adverse intracellular responses. Although fine and ultrafine PM have attracted much attention, the inflammatory damage caused by coarse PM cannot be ignored.

Analysis of the air quality in upper atmospheric boundary layer in a high-density city in Asia using 3-year vertical profiles measured by the 3-Dimensional Real-Time Atmospheric Monitoring System (3DREAMS)

Past studies focused on ground-level air quality, whereas air quality in upper atmospheric boundary layer (ABL) remains unclear due to lack of long-term and high time-resolution profile data. This study utilized the 3-Dimensional Real-Time Atmospheric Monitoring System (3DREAMS) to provide vertical profiles of aerosol backscatter coefficients and wind for three years (2019-2021), along with DustTrak to describe and analyze the characteristics of aerosols in the upper ABL in a high-density city in Asia (Hong Kong, China). It is the first study to assess the long-term record and spatial variations of upper-level aerosol in a high-density city using a LiDAR network. Results show an opposite diurnal profile of aerosol comparing between ground-level and upper ABL, which is different with the diurnal pattern observed in ground measurements (higher air pollutant concentration level in daytime and lower in nighttime). The co-location vertical wind measurements provided the explanation of the opposite diurnal patterns. The 3-year vertical profiles also show the significant spatial variation of vertical distribution of aerosol at different locations, whereas the temporal variations can be affected by various factors such as emissions and transboundary air pollution. Our episode analysis clearly demonstrated the capability of 3DREAMS to monitor transboundary air pollution with detailed information of horizontal transport and vertical convection. 3DREAMS is therefore proved as a suitable system for transboundary air pollution monitoring. Our findings provide a critical reference for atmospheric scientists and decisionmakers to understand transboundary air pollution.

Assessing the Spatiotemporal Characteristics, Factor Importance, and Health Impacts of Air Pollution in Seoul by Integrating Machine Learning into Land-Use Regression Modeling at High Spatiotemporal Resolutions

Previous studies have characterized spatial patterns of air pollution with land-use regression (LUR) models. However, the spatiotemporal characteristics of air pollution, the contribution of various factors to them, and the resultant health impacts have yet to be evaluated comprehensively. This study integrates machine learning (random forest) into LUR modeling (LURF) with intensive evaluations to develop high spatiotemporal resolution prediction models to estimate daily and diurnal PM2.5 and NO2 in Seoul, South Korea, at the spatial resolution of 500 m for a year (2019) and to then evaluate the contribution of driving factors and quantify the resultant premature mortality. Our results show that incorporating the random forest algorithm into our LUR model improves the model performance. Meteorological conditions have a great influence on daily models, while land-use factors play important roles in diurnal models. Our health assessment using dynamic population data estimates that PM2.5 and NO2 pollution, when combined, causes a total of 11,183 (95% CI: 5837-16,354) premature mortalities in Seoul in 2019, of which 64.9% are due to PM2.5, while the remaining are attributable to NO2. The air pollution-attributable health impacts in Seoul are largely caused by cardiovascular diseases including stroke. This study pinpoints the significant spatiotemporal variations and health impact of PM2.5 and NO2 in Seoul, providing essential data for epidemiological research and air quality management.

Emission reduction effects of vertical environmental regulation: Capacity transfer or energy intensity reduction? Evidence from a quasi-natural experiment in China

There is insufficient research on how to reduce the destructive effects of command-based environmental regulation through institutional design. The implementation of the National Key Monitoring Enterprises provides new evidence to assess the effects of vertical monitoring. This study integrates and matches three types of micro databases in China: industrial, pollution, and patent, and constructs firm-level panel data from 2004 to 2010. The empirical evidence shows that the policy reduces the energy use intensity of monitored enterprises by about 10.4% and sulfur dioxide emission intensity by about 23.9%. The mechanism test shows that this effect is achieved by means of energy structure optimization, process innovation, and end-of-pipe treatment, but the effect on total factor productivity is not significant. Among them, the positive impact is stronger for high-profit and emerging firms. Further, we quantify the policy-induced capacity transfer and technology spillovers from monitored enterprises to non-monitored enterprises. In terms of scale, the policy leads to a simultaneous increase in output and pollution emissions of unmonitored firms in the same industry. However, in terms of efficiency, the policy reduces the energy use intensity and pollution emission intensity of enterprises in the same industry.

Winter and spring variation in sources, chemical components and toxicological responses of urban air particulate matter samples in Guangzhou, China

The sources and chemical components of urban air particles exhibit seasonal variations that may affect their hazardousness to human health. Our aims were to investigate winter and spring variation in particulate matter (PM) sources, components and toxicological responses of different PM size fractions from samples collected in Guangzhou, China. Four size-segregated PM samples (PM_10-2.5, PM_2.5-1, PM_1-0.2, and PM_0.2) were collected separately during winter (December 2017 and January 2018) and spring (March 2018). All PM samples were analyzed for chemical components and characterized by source. RAW 264.7 macrophages were exposed to four doses of PM samples for 24 h. Cytotoxicity, oxidation, cell cycle, genotoxicity and inflammatory parameters were tested. PM concentrations were higher in the winter samples and caused more severe cytotoxicity and oxidative damage than to PM in the spring samples. PM in winter and spring led to increases in cell cycle and genotoxicity. The trends of size-segregated PM components were consistent in winter and spring samples. Metallic elements and PAHs were found in the largest concentrations in winter PM, but ions were found in the largest concentrations in spring PM. metallic elements, PAHs and ions in size-segregated PM samples were associated with most toxicological endpoints. Soil dust and biomass burning were the main sources of PM in winter, whereas traffic exhaust and biomass burning was the main source with of spring PM. Our results suggest that the composition of PM samples from Guangzhou differed during winter and spring, which led to strong variations in toxicological responses. The results demonstrate the importance of examining a different particle sizes, compositions and sources across different seasons, for human risk assessment.

Long-term trends of atmospheric hot-and-polluted episodes (HPE) and the public health implications in the Pearl River Delta region of China

Air pollution and extreme heat have been responsible for more than a million deaths in China every year, especially in densely urbanized regions. While previous studies intensively evaluated air pollution episodes and extreme heat events, a limited number of studies comprehensively assessed atmospheric hot-and-polluted-episodes (HPE) - an episode with simultaneously high levels of air pollution and temperature - which have potential adverse synergic impacts on human health. This study focused on the Pearl River Delta (PRD) region of China due to its high temperature in summer and poor air quality throughout a year. We employed geostatistical downscaling to model meteorology at a spatial resolution of 1 km, and applied a machine learning algorithm (XGBoost) to estimate a high-resolution (1 km) daily concentration of particulate matter with an aerodynamic diameter ≤2.5 μm (PM_2.5) and ozone (O₃) for June to October over 20 years (2000-2019). Our results indicate an increasing trend (∼50%) in the frequency of HPE occurrence in the first decade (2000-2010). Conversely, the annual frequency of HPE occurrence reduced (16.7%), but its intensity increased during the second decade (2010-2019). The northern cities in the PRD region had higher levels of PM_2.5 and O₃ than their southern counterparts. During HPEs, regional daily PM_2.5 exceeded the World Health Organization (WHO) and Chinese guideline levels by 75% and 25%, respectively, while the O₃ exceeded the WHO O₃ standard by up to 69%. Overall, 567,063 (95% confidence interval (CI): 510,357-623,770) and 52,231 (95%CI: 26,116-78,346) excessive deaths were respectively attributable to exposure to PM_2.5 and O₃ in the PRD region. Our findings imply the necessity and urgency to formulate co-benefit policies to mitigate the region's air pollution and heat problems.

Effect of Vertical Wind Shear on PM2.5 Changes over a Receptor Region in Central China

Vertical wind shear (VWS) significantly impacts the vertical mixing of air pollutants and leads to changes in near-surface air pollutants. We focused on Changsha (CS) and Jingmen (JM), the upstream and downstream urban sites of a receptor region in central China, to explore the impact of VWS on surface PM2.5 changes using 5-year wintertime observations and simulations from 2016–2020. The surface PM2.5 concentration was lower in CS with higher anthropogenic PM2.5 emissions than in JM, and the correlation between wind speed and PM2.5 was negative for clean conditions and positive for polluted conditions in both two sites. The difference in the correlation pattern of surface PM2.5 and VWS between CS and JM might be due to the different influences of regional PM2.5 transport and boundary layer dynamics. In downstream CS, the weak wind and VWS in the height of 1–2 km stabilized the ABL under polluted conditions, and strong northerly wind accompanied by enhanced VWS above 2 km favored the long-range transport of air pollutants. In upstream JM, local circulation and long-range PM2.5 transport co-determined the positive correlation between VWS and PM2.5 concentrations. Prevailed northerly wind disrupted the local circulation and enhanced the surface PM2.5 concentrations under polluted conditions, which tend to be an indicator of regional transport of air pollutants. The potential contribution source maps calculated from WRF-FLEXPART simulations also confirmed the more significant contribution of regional PM2.5 transport to the PM2.5 pollution in upstream region JM. By comparing the vertical profiles of meteorological parameters for typical transport- and local-type pollution days, the northerly wind prevailed throughout the ABL with stronger wind speed and VWS in transport-type pollution days, favoring the vertical mixing of transported air pollutants, in sharp contrast to the weak wind conditions in local-type pollution days. This study provided the evidence that PM2.5 pollution in the Twain-Hu Basin was affected by long-distance transport with different features at upstream and downstream sites, improving the understanding of the air pollutant source–receptor relationship in air quality changes with regional transport of air pollutants.

What drives long-term PM2.5-attributable premature mortality change? A case study in central China using high-resolution satellite data from 2003 to 2018

Ambient PM_2.5 was reported to be related to numerous negative health outcomes, leading to adverse public health impacts in many countries such as China. Despite the apparent reduction in PM_2.5 levels over China due to its emission control policies in recent years, the health burdens were not reduced as much as expected. This calls for a comprehensive analysis to explain the reasons behind to provide a useful reference for formulating effective emission control strategies. Taking central China as an example due to its large population and high levels of PM_2.5, this study quantified the spatiotemporal dynamics of premature mortality associated with PM_2.5 pollution in central China for each year during 2003-2018 and applied a decomposition analysis to dissect the contribution of various driving factors including ambient PM_2.5 level, demographic distribution and baseline incidence rate of four diseases related to air pollution. Results show significant spatiotemporal variations in PM_2.5-attributed health impact in central China, including Henan, Hubei, and Hunan provinces. Five Henan cities had the largest PM_2.5-attributable premature mortality (∼8-12 K premature mortalities), while three Hubei cities and one Hebei city had the least chronic PM_2.5-related all-cause mortality numbers (<1 K mortalities). Throughout the study period, the PM_2.5-caused premature mortality decreased by 54 K, in which changes in PM_2.5 levels and baseline incidence rates of stroke and chronic obstructive pulmonary disease contributed to the positive effect, whereas demographic changes and baseline incidence rate change of ischemic heart disease and lung cancer brought a countervailing effect. Our findings suggest more dynamic and comprehensive policies and measures that take into account spatiotemporal variations of health burden for effective alleviation of the health impact of PM_2.5 pollution in the country.

Influence of a weak typhoon on the vertical distribution of air pollution in Hong Kong: A perspective from a Doppler LiDAR network

High particulate matter (PM) and ozone (O₃) concentration in Hong Kong are frequently observed during the summertime typhoon season. Despite the critical effect of a typhoon on air pollution, contributions of vertical wind profile and cloud movement during transboundary air pollution (TAP) on surface PM and O₃ concentration have yet to be fully understood. This work is the first study to apply a network of Doppler light detection and ranging (LiDAR) as well as back trajectory analysis to comprehensively analyze the effect of a weak Typhoon (Danas) occurring during 16-19 July 2019 on different variations in PM and O₃ concentration. During the typhoon Danas, three types of surface air pollution with five episodes were identified: (1) low PM and high O₃ concentration; (2) co-occurring high PM and O₃ concentration and (3) high PM and low O₃ concentration. Employing our 3D Real-Time Atmospheric Monitoring System (3DREAMs) along with surface observations, we found the important role of TAP in the increases in surface PM and O₃ concentration with significant vertical wind shear that transported air pollutants at upper levels, and strong vertical mixing that brought air pollutants to the ground level. Cloud movement related to typhoon periphery, as well as high solar radiation due to sinking motion and remote transport by continental wind, have an impact on local O₃ concentration. For the substantial difference in O₃ concentration between two air quality measurement sites, the similar vertical aerosol distributions and wind profiles suggest the comparable TAP contributions at the two sites and thus infer the critical role of local O₃ photochemical process in the O₃ difference. This work comprehensively reveals the influences of a weak typhoon on variations in PM and O₃ during the five episodes, providing important references for air quality monitoring and forecast in regions under the influence of typhoon.

Air Pollution Assessment in China: A Novel Group Multiple Criteria Decision Making Model under Uncertain Information

Assessment of and controlling air pollution are urgent global issues where international cooperation is deemed necessary. Although a very relevant data source can be obtained through continuous monitoring of air quality, measuring air pollutant concentrations is quite difficult when compared to other environmental indicators. We mainly have three different aims for the current study: (1) we propose the computation of the interval weights of decision makers (DMs) based on a group multiple criteria decision making (GMCDM) model; (2) we aim to rank the overall preferences of DMs by the possibility concepts; (3) we aim to evaluate the air quality in China using the most recent data based on our proposed method. We consider three monitoring stations, namely Luhu Park, Wanqingsha, and Tianhu, and the data for SO2, NO2, and PM10 are collected for November 2017, 2018, and 2019. The results from our innovative model show that November 2019 had the best air quality. Finally, robustness analyses are also performed to confirm the discriminatory power of the proposed approach.

A practical framework for predicting residential indoor PM2.5 concentration using land-use regression and machine learning methods

People typically spend most of their time indoors. It is of importance to establish prediction models to estimate PM_2.5 concentration in indoor environments (e.g., residential households) to allow accurate assessments of exposure in epidemiological studies. This study aimed to develop models to predict PM_2.5 concentration in residential households. PM_2.5 concentration and related parameters (e.g., basic information about the households and ventilation settings) were collected in 116 households during the winter and summer seasons in Hong Kong. Outdoor PM_2.5 concentration at households was estimated using a land-use regression model. The random forest machine learning algorithm was then applied to develop indoor PM_2.5 prediction models. The results show that the random forest model achieved a promising predictive accuracy, with R² and cross-validation R² values of 0.93 and 0.65, respectively. Outdoor PM_2.5 concentration was the most important predictor variable, followed in descending order by the household marked number, outdoor temperature, outdoor relative humidity, average household area and air conditioning. The external validation result using an independent dataset confirmed the potential application of the random forest model, with an R² value of 0.47. Overall, this study shows the value of a combined land-use regression and machine learning approach in establishing indoor PM_2.5 prediction models that provide a relatively accurate assessment of exposure for use in epidemiological studies.

The varying driving forces of PM2.5 concentrations in Chinese cities: Insights from a geographically and temporally weighted regression model

BACKGROUND

Particulate pollution is currently regarded as a severe environmental problem, which is intimately linked to reductions in air quality and human health, as well as global climate change.

OBJECTIVE

Accurately identifying the key factors that drive air pollution is of great significance. The temporal and spatial heterogeneity of such factors is seldom taken into account in the existing literature.

METHOD

In this study, we adopted a geographically and temporally weighted regression model (GTWR) to explore the direction and strength of the influences of natural conditions and socioeconomic issues on the occurrence of PM_2.5 pollutions in 287 Chinese cities covering the period 1998 to 2015.

RESULT

Cities with serious PM_2.5 pollution were discovered to mainly be situated in northern China, whilst cities with less pollution were shown to be located in southern China. Higher temperature and wind speed were found to be able to alleviate air pollution in the country's southeast, where enhanced precipitation was also shown to reduce PM_2.5 concentrations; whilst in southern and central and western regions, precipitation and PM_2.5 concentrations were positively correlated. Increased relative humidity was found to reinforce PM_2.5 concentration in southwest and northeast China. Furthermore, per capita GDP and population density were shown to intensify PM_2.5 concentrations in northwest China, inversely, they imposed a substantial adverse effect on PM_2.5 concentration levels in other areas. The amount of urban built-up area was more positively associated with PM_2.5 concentration levels in southeastern cities than in other cities in China.

CONCLUSION

PM_2.5 concentrations conformed to a series of stages and demonstrated distinct spatial differences in China. The associations between PM_2.5 concentration levels and their determinants exhibit obvious spatial heterogeneity. The findings of this paper provide detailed support for regions to formulate targeted emission mitigation policies.

Air pollution characteristics and human health risks in key cities of northwest China

Air pollution events occur frequently in northwest China, which results in serious detrimental effects on human health. Therefore, it is essential to understand the air pollution characteristics and assess the risks to humans. In this study, we analyzed the pollution characteristics of criteria pollutants in six key cities in northwest China from 2015 to 2018. We used the air quality index (AQI), aggregate AQI (AAQI), and health-risk based AQI (HAQI) to assess the health risks and determine the proportion of people exposed to air pollution. Additionally, on this basis, the AirQ_2.2.3 model was used to quantify the health effects of the pollutants. The results showed that PM₁₀ pollution occurred mainly in spring and winter and was caused by frequent dust storms. PM_2.5 pollution was caused mainly by anthropogenic activities (especially coal-fired heating in winter). Because of a series of government policies and pollutant reduction measures, PM_2.5, SO₂, NO₂, and CO concentrations showed a downward trend during the study period (except for a small increase in the case of NO₂ in some years.). However, O₃ showed high concentrations due to the high intensity of solar radiation in summer and inadequate emission reduction measures. The air quality levels based on their classification were generally higher than the Chinese ambient air quality standard classified by the AQI index. We also found that the higher the AQI index was, the more serious the air pollution classified based on the AAQI and HAQI indices was. The HAQI index could better reflect the impact of pollutants on human health. Based on the HAQI index, 20% of the population in the study area was exposed to polluted air. The total mortality values attributable to PM₁₀, PM_2.5, SO₂, O₃, NO₂, and CO, quantified by the AirQ_2.2.3 model, were 3.00%, 1.02%, 1.00%, 4.22%, 1.57%, and 0.95% (Confidence Interval:95%), respectively; the attributable proportions of mortality for respiratory system and cardiovascular diseases were consistent with the change rule of total mortality, because the number of deaths attributable to the latter was greater than that for the former. According to the exposure reaction curves of pollutants, PM₁₀ and PM_2.5 still showed a large change at high concentrations. However, the tendencies of SO₂, NO₂, CO, and O₃ were more obvious under low concentration exposure, which indicated that the expected mortality rate due to lower air pollution concentrations was much higher than the mortality due to high air pollution concentrations.

Factors influencing the boundary layer height and their relationship with air quality in the Sichuan Basin, China

We investigated the factors influencing the daily maximum boundary layer height (h_max) and their relationship with air quality in the Sichuan Basin, China. We analyzed the factors influencing h_max on cloudy and sunny days in winter using five years of observational data and a reanalysis dataset and investigated the relationship between h_max and air quality. The inversion layer in the lower troposphere has a critical impact on h_max on cloudy days. By contrast, the sensible heat flux and wind shear are the main influencing factors on sunny days, although the contribution of the sensible heat flux to h_max is less than that of the wind shear. This is because the turbulence is mainly affected by mechanical mixing induced by the topographic effect of the Tibetan Plateau to the west of the Sichuan Basin. The secondary circulation over the Sichuan Basin is weaker on cloudy days than on sunny days. These results are important for understanding the dispersion of air pollutants over the Sichuan Basin.

The Urban–Rural Heterogeneity of Air Pollution in 35 Metropolitan Regions across China

Urbanization and air pollution are major anthropogenic impacts on Earth’s environment, weather, and climate. Each has been studied extensively, but their interactions have not. Urbanization leads to a dramatic variation in the spatial distribution of air pollution (fine particles) by altering surface properties and boundary-layer micrometeorology, but it remains unclear, especially between the centers and suburbs of metropolitan regions. Here, we investigated the spatial variation, or inhomogeneity, of air quality in urban and rural areas of 35 major metropolitan regions across China using four different long-term observational datasets from both ground-based and space-borne observations during the period 2001–2015. In general, air pollution in summer in urban areas is more serious than in rural areas. However, it is more homogeneously polluted, and also more severely polluted in winter than that in summer. Four factors are found to play roles in the spatial inhomogeneity of air pollution between urban and rural areas and their seasonal differences: (1) the urban–rural difference in emissions in summer is slightly larger than in winter; (2) urban structures have a more obvious association with the spatial distribution of aerosols in summer; (3) the wind speed, topography, and different reductions in the planetary boundary layer height from clean to polluted conditions have different effects on the density of pollutants in different seasons; and (4) relative humidity can play an important role in affecting the spatial inhomogeneity of air pollution despite the large uncertainties.

Prediction model for air particulate matter levels in the households of elderly individuals in Hong Kong

Air pollution has shown to cause adverse health effects on mankind. Aging causes functional decline and leaves elderly people more susceptible to health threats associated with air pollution exposure. Elderly spend approximately 80% of their lifetime at home every day. To understand air pollution exposure, indoor air pollutants are the targets for consideration especially for the elderly population. However, indoor air monitoring for epidemiological studies requires a large population, is labor intensive and time consuming. As a result, a prediction model is necessary. For 3 consecutive days in summer and winter, 24-h average of mass concentrations of fine particulate matter (aerodynamic diameter <2.5 μm: PM_2.5) were measured in indoors for 116 households. A PM_2.5 prediction model for elderly households in Hong Kong has been developed by combining ambient PM_2.5 concentrations obtained from land use regression model and questionnaire-elicited information related to the indoor PM_2.5 sources. The fitted linear mixed-effects model is moderately predictive for the observed indoor PM_2.5, with R² = 0.67 (or R² = 0.61 by cross-validation). The model shows indoor PM_2.5 was positively influenced by outdoor PM_2.5 levels. Meteorological factors (e.g. temperature and relative humidity) were related to the indoor PM_2.5 in a relatively complex manner. Congested living areas, opening windows for extended periods for ventilation and use of liquefied petroleum gas for cooking were the factors determining the ultimate indoor air quality. This study aims to provide information about controlling household air quality and can be used for future epidemiological studies associated with indoor air pollution in large population.

Observation of Turbulent Mixing Characteristics in the Typical Daytime Cloud-Topped Boundary Layer over Hong Kong in 2019

Turbulent mixing is critical in affecting urban climate and air pollution. Nevertheless, our understanding of it, especially in a cloud-topped boundary layer (CTBL), remains limited. High-temporal resolution observations provide sufficient information of vertical velocity profiles, which is essential for turbulence studies in the atmospheric boundary layer (ABL). We conducted Doppler Light Detection and Ranging (LiDAR) measurements in 2019 using the 3-Dimensional Real-time Atmospheric Monitoring System (3DREAMS) to reveal the characteristics of typical daytime turbulent mixing processes in CTBL over Hong Kong. We assessed the contribution of cloud radiative cooling on turbulent mixing and determined the altitudinal dependence of the contribution of surface heating and vertical wind shear to turbulent mixing. Our results show that more downdrafts and updrafts in spring and autumn were observed and positively associated with seasonal cloud fraction. These results reveal that cloud radiative cooling was the main source of downdraft, which was also confirmed by our detailed case study of vertical velocity. Compared to winter and autumn, cloud base heights were lower in spring and summer. Cloud radiative cooling contributed ~32% to turbulent mixing even near the surface, although the contribution was relatively weaker compared to surface heating and vertical wind shear. Surface heating and vertical wind shear together contributed to ~45% of turbulent mixing near the surface, but wind shear can affect up to ~1100 m while surface heating can only reach ~450 m. Despite the fact that more research is still needed to further understand the processes, our findings provide useful references for local weather forecast and air quality studies.

Development of a 3D Real-Time Atmospheric Monitoring System (3DREAMS) Using Doppler LiDARs and Applications for Long-Term Analysis and Hot-and-Polluted Episodes

Heatwaves and air pollution are serious environmental problems that adversely affect human health. While related studies have typically employed ground-level data, the long-term and episodic characteristics of meteorology and air quality at higher altitudes have yet to be fully understood. This study developed a 3-Dimensional Real-timE Atmospheric Monitoring System (3DREAMS) to measure and analyze the vertical profiles of horizontal wind speed and direction, vertical wind velocity as well as aerosol backscatter. The system was applied to Hong Kong, a highly dense city with complex topography, during each season and including hot-and-polluted episodes (HPEs) in 2019. The results reveal that the high spatial wind variability and wind characteristics in the lower atmosphere in Hong Kong can extend upwards by up to 0.66 km, thus highlighting the importance of mountains for the wind environment in the city. Both upslope and downslope winds were observed at one site, whereas downward air motions predominated at another site. The high temperature and high concentration of fine particulate matter during HPEs were caused by a significant reduction in both horizontal and vertical wind speeds that established conditions favorable for heat and air pollutant accumulation, and by the prevailing westerly wind promoting transboundary air pollution. The findings of this study are anticipated to provide valuable insight for weather forecasting and air quality studies. The 3DREAMS will be further developed to monitor upper atmosphere wind and air quality over the Greater Bay Area of China.

The association between PM2.5 exposure and suicidal ideation: a prefectural panel study

Background

Suicidal ideation is subject to serious underestimation among existing public health studies. While numerous factors have been recognized in affecting suicidal thoughts and behaviors (STB), the associated environmental risks have been poorly understood. Foremost among the various environment risks were air pollution, in particular, the PM2.5. The present study attempted to examine the relationship between PM_2.5 level and local weekly index of suicidal ideation (ISI).

Methods

Using Internet search query volumes in Baidu (2017), the largest internet search engine in China, we constructed a prefectural panel data (278 prefectures, 52 weeks) and employed dynamic panel GMM system estimation to analyze the relationship between weekly concentration of PM2.5 (Mean = 87 μg·m^− 3) and the index of suicidal ideation (Mean = 49.9).

Results

The results indicate that in the spring and winter, a 10 μg·m^− 3 increase in the prior week’s PM_2.5 in a Chinese city is significantly associated with 0.020 increase in ISI in spring and a 0.007 increase in ISI in winter, after taking account other co-pollutants and meteorological conditions.

Conclusion

We innovatively proposed the measure of suicidal ideation and provided suggestive evidence of a positive association between suicidal ideation and PM_2.5 level.

Vertical Wind Shear Modulates Particulate Matter Pollutions: A Perspective from Radar Wind Profiler Observations in Beijing, China

Vertical wind shear (VWS) is one of the key meteorological factors in modulating ground-level particulate matter with an aerodynamic diameter of 2.5 µm or less (PM2.5). Due to the lack of high-resolution vertical wind measurements, how the VWS affects ground-level PM2.5 remains highly debated. Here we employed the wind profiling observations from the fine-time-resolution radar wind profiler (RWP), together with hourly ground-level PM2.5 measurements, to explore the wind features in the planetary boundary layer (PBL) and their association with aerosols in Beijing for the period from December 1, 2018, to February 28, 2019. Overall, southerly wind anomalies almost dominated throughout the whole PBL or even beyond the PBL under polluted conditions during the course of a day, as totally opposed to the northerly wind anomalies in the PBL under clean conditions. Besides, the ground-level PM2.5 pollution exhibited a strong dependence on the VWS. A much weaker VWS was observed in the lower part of the PBL under polluted conditions, compared with that under clean conditions, which could be due to the strong ground-level PM2.5 accumulation induced by weak vertical mixing in the PBL. Notably, weak northbound transboundary PM2.5 pollution mainly appeared within the PBL, where relatively small VWS dominated. Above the PBL, strong northerlies winds also favored the long-range transport of aerosols, which in turn deteriorated the air quality in Beijing as well. This was well corroborated by the synoptic-scale circulation and backward trajectory analysis. Therefore, we argued here that not only the wind speed in the vertical but the VWS were important for the investigation of aerosol pollution formation mechanism in Beijing. Also, our findings offer wider insights into the role of VWS from RWP in modulating the variation of PM2.5, which deserves explicit consideration in the forecast of air quality in the future.

Mapping ozone source-receptor relationship and apportioning the health impact in the Pearl River Delta region using adjoint sensitivity analysis

While fine particulate matters are decreasing in the Pearl River Delta (PRD) region, the regional ozone (O₃) shows an increasing trend that affects human health, leading to an urgent need for scientific understanding of source-receptor relationship between O₃ and its precursor emissions given the changing background composition. We advanced and applied an adjoint air quality model to map contributions of individual O₃ precursor emission sources [nitrogen oxides (NO_x) and volatile organic compound (VOC)] at each location to annual regional O₃ concentrations and to identify the possible dominant influential pathways of emission sources to O₃ at different spatiotemporal scales. Additionally, we introduced the novel adjoint sensitivity approach to assess the relationship between precursor emissions and O₃-induced premature mortality. Adjoint results show that Shenzhen was a major source contributor to regional O₃ throughout all seasons, of which 49.4% (3.8%) were from its NO_x (VOC) emissions. Local emissions (within PRD) contributed to 83% of the regional O₃ whereas only ~54% of the estimated ~4000 regional O₃-induced premature mortalities. The discrepancy between these two contributions was because O₃-induced mortalities are dependent on not only O₃ concentration, but incident rate and population density. We also found that a city with low O₃-induced mortalities could have significant emission contributions to health impact in the region since the transport pathways could be through transport of local O₃ or through transport of O₃ precursors that form regional O₃ thereafter. It is therefore necessary to formulate emission control policies from both air quality and public health perspectives, and it is also critical to have better understanding of influential pathways of emission sources to O₃.

Revealing the impacts of transboundary pollution on PM2.5-related deaths in China

Long-range transport of air pollutants may cause significant health impacts in the receptor regions. In this study, we calculated the transboundary health impact from different foreign regions using a state-of-the-art air quality model at hemispheric scale. Our results reveal that transboundary PM_2.5 pollution from outside China was of great significance, causing 100 thousand (95% CI, 45 thousand-200 thousand) premature deaths in China in 2015, which accounted for 9.60% PM_2.5 related premature death in China. The impact of transboundary pollution in China was most significant in winter, in which the average PM_2.5 concentration increased by 3.7 μg/m³, and was least significant in summer, with the average PM_2.5 concentration increasing by 0.5 μg/m³. Liaoning and Yunnan provinces were extremely susceptible to transboundary pollution, whose annual average PM_2.5 concentrations were increased by 10.2 and 11.4 μg/m³ respectively. Among all foreign regions, the impact from South Asia was most significant, causing 30 thousand (95% CI, 12 thousand-62 thousand) premature deaths annually in China. This study only reveals the transboundary impact under the integrated exposure-response (IER) model and fixed meteorology field in 2015. Further studies are needed to investigate how different exposure-response functions and meteorology affect the transboundary PM_2.5 pollution and its related death.

Risk Analysis of Air Pollution and Meteorological Factors Affecting the Incidence of Diabetes in the Elderly Population in Northern China

BACKGROUND

Research investigating the effect of air pollution on diabetes incidence is mostly conducted in Europe and the United States and often produces conflicting results. The link between meteorological factors and diabetes incidence remains to be explored. We aimed to explore associations between air pollution and diabetes incidence and to estimate the nonlinear and lag effects of meteorological factors on diabetes incidence.

METHODS

Our study included 19,000 people aged ≥60 years from the Binhai New District without diabetes at baseline. The generalized additive model (GAM) and the distributed lag nonlinear model (DLNM) were used to explore the effect of air pollutants and meteorological factors on the incidence of diabetes. In the model combining the GAM and DLNM, the impact of each factor (delayed by 30 days) was first observed separately to select statistically significant factors, which were then incorporated into the final multivariate model. The association between air pollution and the incidence of diabetes was assessed in subgroups based on age, sex, and body mass index (BMI).

RESULTS

We found that cumulative RRs for diabetes incidence were 1.026 (1.011-1.040), 1.019 (1.012-1.026), and 1.051 (1.019-1.083) per 10 μg/m³ increase in PM_2.5, PM₁₀, and NO₂, respectively, as well as 1.156 (1.058-1.264) per 1 mg/m³ increase in CO in a single-pollutant model. Increased temperature, excessive humidity or dryness, and shortened sunshine duration were positively correlated with the incidence of diabetes in single-factor models. After adjusting for temperature, humidity, and sunshine, the risk of diabetes increased by 9.2% (95% confidence interval (CI):2.1%-16.8%) per 10 μg/m³ increase in PM_2.5. We also found that women, the elderly (≥75 years), and obese subjects were more susceptible to the effect of PM_2.5.

CONCLUSION

Our data suggest that PM_2.5 is positively correlated with the incidence of diabetes in the elderly, and the relationship between various meteorological factors and diabetes in the elderly is nonlinear.

A Study of the Socioeconomic Forces Driving Air Pollution Based on a DPSIR Model in Henan Province, China

The 3D spatiotemporal distribution (spatial and annual-month-daily temporal) features of the air quality index (AQI), air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3-8h), and air pollution risks (R) from 2003 to 2018 were investigated to understand the severity of air pollution in Henan province. The multiyear ascending trends for AQI and R values, with a peak in 2015, were observed in 2003 to 2018 since the annual population-weighted average concentrations of PM2.5, PM10, NO2, and O3-8h were always higher than the CAAQs II from 2013 to 2017 in Henan province. Changes in the monthly moving average AQI values in Henan province conformed to a U-shaped pattern, with the highest values in the winter (from December to February) and the lowest in the summer (from June to August). Triple peaks for AQI values of 8:00‒10:00 a.m., 6:00‒8:00 p.m., and 2:00‒4:00 p.m. in the representative municipalities corresponded with the morning and evening traffic tendencies and photochemical process. A spatial analysis indicated that there were decreasing trends for air pollution from northwest to southeast in Henan province. Data on 48 concrete parameters were collected from 2003 to 2017 to construct a driving force‒pressure‒state‒influence‒response (DPSIR) model for assessing the socioeconomic forces driving air pollution in this province. It was the too-rapid growth of the driving force index (DFI), induced by urban development and population growth (UDPG), economic growth and change of industrial structure (EGCIS), and energy consumption growth and structure change (ECGSC), that led to a direct increase in the atmospheric pollution burden, i.e., total emissions from air pollution and industrial emissions, which are linearly correlated to values of UDPG and ECGSC, respectively (p < 0.05). Furthermore, the prediction models for AQI and R values in Henan province, with the growth rates being 4.251 DFI−1 and 0.0816 DFI−1, respectively, were simulated by multiple linear regression analysis. Therefore, the integrated risks of air pollution in Henan province were originally driven by DFI.

The Use of the Internet of Things for Estimating Personal Pollution Exposure

This paper proposes a framework for an Air Quality Decision Support System (AQDSS), and as a proof of concept, develops an Internet of Things (IoT) application based on this framework. This application was assessed by means of a case study in the City of Madrid. We employed different sensors and combined outdoor and indoor data with spatiotemporal activity patterns to estimate the Personal Air Pollution Exposure (PAPE) of an individual. This pilot case study presents evidence that PAPE can be estimated by employing indoor air quality monitors and e-beacon technology that have not previously been used in similar studies and have the advantages of being low-cost and unobtrusive to the individual. In future work, our IoT application can be extended to include prediction models, enabling dynamic feedback about PAPE risks. Furthermore, PAPE data from this type of application could be useful for air quality policy development as well as in epidemiological studies that explore the effects of air pollution on certain diseases.

Source contributions of surface ozone in China using an adjoint sensitivity analysis

Air pollution has become an adverse environmental problem in China, resulting in serious public health impacts. This study advanced and applied the CMAQ adjoint model to quantitatively assess the source-receptor relationships between surface ozone (O₃) changes over different receptor regions and precursor emissions across all locations in China. Five receptor regions were defined based on the administrative division, including northern China (NC), southern China (SC), Pearl River Delta region (PRD), Yangtz River Delta region (YRD), and Beijing-Tianjin-Hebei region (BTH). Our results identified the different influential pathways of atmospheric processes and emissions to O₃ pollution. We found that the atmospheric processes such as horizontal and vertical advection could offset the O₃ removal through chemical reactions in VOC-limited areas inside the receptor regions. In addition, O₃ pollution can be induced by transport of O₃ directly or its precursors. Our results of relative source contributions to O₃ show that transboundary O₃ pollution was significant in SC, NC and YRD, while the O₃ pollution in PRD and BTH were more contributed by local sources. Anhui, Hubei and Jiangsu provinces were the three largest source areas of NO_x and VOC emissions to O₃ in SC (>52%) and YRD (>69%). NO_x and VOC emissions from Tianjin and Beijing were the largest contributors to O₃ in NC (>34%) and BTH (>51%). PRD was the dominant source areas of NO_x (>89%) and VOC emissions (~98%) to its own regional O₃.

Emission Tax and Compensation Subsidy with Cross-Industry Pollution

This paper establishes a cross-industry pollution externality model. To explain a benevolent government, it may be possible to tax part of the welfare gains and use the revenue to compensate the affected polluted industry for the damage cost, thereby improving welfare. We show that the social welfare under emission tax with production subsidy is higher than the results of emission tax without production subsidy. The welfare of the polluted sector under emissions trading will be lower than the results of unbalanced budget environmental policy with subsidy. The welfare of the polluted labor union under lobby for compensation will be higher than the results of environmental policy with subsidy if the pollution damage and the weight on political contributions are sufficiently high.

Economic impacts from PM2.5 pollution-related health effects in China's road transport sector: A provincial-level analysis

Economic impact assessments of air pollution-related health effects from a sectoral perspective in China is still deficient. This study evaluates the PM_2.5 pollution-related health impacts of the road transport sector on China's economy at both national and provincial levels in 2030 under various air mitigation technologies scenarios. Health impacts are estimated using an integrated approach that combines the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model, a computable general equilibrium (CGE) model and a health model. Results show that at a national level, the road transport sector leads to 163.64 thousand deaths per year, increases the per capita risk of morbidity by 0.37% and accounts for 1.43 billion Yuan in health care expenditures. We estimate 442.90 billion Yuan of the value of statistical life loss and 2.09 h/capita of work time loss in 2015. Without additional control measures, air pollution related to the transport sector will cause 177.50 thousand deaths in 2030, a 0.40% per capita increase in the risk of morbidity, accounting for 4.12 billion Yuan in health care expenditures, 737.15 billion Yuan of statistical life loss and 2.23 h/capita of work time loss. Based on our model, implementing the most strict control strategy scenario would decrease mortality by 42.14%, morbidity risk by 42.14%, health care expenditures by 41.94%, statistical life loss by 26.22% and hours of work time loss by 42.65%, comparing with the no control measure scenario. In addition, PM_2.5 pollution from the road transport sector will cause 0.68% GDP loss in 2030. At a provincial level, GDP losses in 14 out of 30 provinces far exceed the national rate. Henan (1.20%), Sichuan (1.07%), Chongqing (0.99%), Hubei (0.94%), and Shandong (0.90%) would experience the highest GDP loss in 2030. Implementing control strategies to reduce PM_2.5 pollution in the road transport sector could bring positive benefits in half of the Chinese provinces especially in provinces that suffer greater health impacts from the road transport sector (such as Henan and Sichuan).

Mixed strategy to allocate resources with air pollution treatment in China: based on the analytic network process and large-group decision-making method

The resource allocation of air pollution treatment in China is a complex problem, since many alternatives are available and many criteria influence mutually. A number of stakeholders participate in this issue holding different opinions because of the benefits they value. So a method is needed, based on the analytic network process (ANP) and large-group decision-making (LGDM), to rank the alternatives considering interdependent criteria and stakeholders' opinions. In this method, the criteria related to air pollution treatment are examined by experts. Then, the network structure of the problem is constructed based on the relationships between the criteria. Further, every participant in each group provide comparison matrices by judging the importance between criteria according to dominance, regarding a certain criteria (or goal), and the geometric average comparison matrix of each group is obtained. The decision weight of each group is derived by combining the subjective weight and the objective weight, in which the subjective weight is provided by organizers, while the objective weight is determined by considering the consensus levels of groups. The final comparison matrices are obtained by the geometric average of comparison matrices and the decision weights. Next, the resource allocation is made according to the priorities of the alternatives using the super decision software. Finally, an example is given to illustrate the use of the proposed method.