Addition of PM 2.5 into the national ambient air quality standards of China and the contribution to air pollution control: the case study of Wuhan, China

Exploring the Burden of PM2.5-Related Deaths and Economic Health Losses in Beijing

Air pollution is one of the major global public health challenges. Using annual fine particulate matter (PM2.5) concentration data from 2016 to 2021, along with the global exposure mortality model (GEMM), we estimated the multi-year PM2.5-pollution-related deaths divided by different age groups and diseases. Then, using the VSL (value of statistical life) method, we assessed corresponding economic losses and values. The number of deaths attributed to PM2.5 in Beijing in 2021 fell by 33.74 percent from 2016, while health economic losses would increase by USD 4.4 billion as per capita disposable income increases year by year. In 2021, the average annual concentration of PM2.5 in half of Beijing's municipal administrative districts is less than China's secondary ambient air quality standard (35 μg/m3), but it can still cause 48,969 deaths and corresponding health and economic losses of USD 16.31 billion, equivalent to 7.9 percent of Beijing's GDP. Therefore, it is suggested that more stringent local air quality standards should be designated to protect public health in Beijing.

Combining Sun-Photometer, PM Monitor and SMPS to Inverse the Missing Columnar AVSD and Analyze Its Characteristics in Central China

Columnar aerosol volume size distribution (AVSD) is an important atmospheric parameter that shows aerosol microphysical properties and can be used to analyze the impact of aerosols on the radiation budget balance, as well as regional climate effects. Usually, columnar AVSD can be obtained by using a sun photometer, but its observation conditions are relatively strict, and the columnar AVSD will be missing in cloudy or hazy weather due to cloud cover and other factors. This study introduces a novel algorithm for inversion of missing columnar AVSD under haze periods by using a machine learning approach and other ground-based observations. The principle is as follows. We are based on joint observational experiments. Since the scanning mobility particle sizer (SMPS) and particulate matter (PM) monitor sample the surface data, they can be stitched together to obtain the surface AVSD according to their observation range. Additionally, the sun-photometer scans the whole sky, so it can obtain columnar AVSD and aerosol optical depth (AOD). Then we use the back propagation neural network (BPNN) model to establish the relationship between the surface AVSD and the columnar AVSD and add AOD as a constraint. Next, the model is trained with the observation data of the same period. After the model training is completed, the surface AVSD and AOD can be used to invert the missing columnar AVSD during the haze period. In experiments on the 2015 dataset, the results show that the correlation coefficient and root mean square error between our model inversion results and the original sun photometer observations were 0.967 and 0.008 in winter, 0.968 and 0.010 in spring, 0.969 and 0.013 in summer, 0.972 and 0.007 in autumn, respectively. It shows a generally good performance that can be applied to the four seasons. Furthermore, the method was applied to fill the missing columnar AVSD of Wuhan, a city in central China, under adverse weather conditions. The final results were shown to be consistent with the climatic characteristics of Wuhan. Therefore, it can indeed solve the problem that sun photometer observations are heavily dependent on weather conditions, contributing to a more comprehensive study of the effects of aerosols on climate and radiation balance.

Estimation and Analysis of PM2.5 Concentrations with NPP-VIIRS Nighttime Light Images: A Case Study in the Chang-Zhu-Tan Urban Agglomeration of China

Rapid economic and social development has caused serious atmospheric environmental problems. The temporal and spatial distribution characteristics of PM_2.5 concentrations have become an important research topic for sustainable social development monitoring. Based on NPP-VIIRS nighttime light images, meteorological data, and SRTM DEM data, this article builds a PM_2.5 concentration estimation model for the Chang-Zhu-Tan urban agglomeration. First, the partial least squares method is used to calculate the nighttime light radiance, meteorological elements (temperature, relative humidity, and wind speed), and topographic elements (elevation, slope, and topographic undulation) for correlation analysis. Second, we construct seasonal and annual PM_2.5 concentration estimation models, including multiple linear regression, support random forest, vector regression, Gaussian process regression, etc., with different factor sets. Finally, the accuracy of the PM_2.5 concentration estimation model that results in the Chang-Zhu-Tan urban agglomeration is analyzed, and the spatial distribution of the PM_2.5 concentration is inverted. The results show that the PM_2.5 concentration correlation of meteorological elements is the strongest, and the topographic elements are the weakest. In terms of seasonal estimation, the spring estimation results of multiple linear regression and machine learning estimation models are the worst, the winter estimation results of multiple linear regression estimation models are the best, and the annual estimation results of machine learning estimation models are the best. At the same time, the study found that there is a significant difference in the temporal and spatial distribution of PM_2.5 concentrations. The methods in this article overcome the high cost and spatial resolution limitations of traditional large-scale PM_2.5 concentration monitoring, to a certain extent, and can provide a reference for the study of PM_2.5 concentration estimation and prediction based on satellite remote sensing technology.

Estimation and Analysis of the Nighttime PM2.5 Concentration Based on LJ1-01 Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

At present, fine particulate matter (PM2.5) has become an important pollutant in regard to air pollution and has seriously harmed the ecological environment and human health. In the face of increasingly serious PM2.5 air pollution problems, feasible large-scale continuous spatial PM2.5 concentration monitoring provides great practical value and potential. Based on radiative transfer theory, a correlation model of the nighttime light radiance and ground PM2.5 concentration is established. A multiple linear regression model is proposed with the light radiance, meteorological elements (temperature, relative humidity, and wind speed) and terrain elements (elevation, slope, and terrain relief) as variables to estimate the ground PM2.5 concentration at 56 air quality monitoring stations in the Pearl River Delta (PRD) urban agglomeration from 2018 to 2019, and the accuracy of model estimation is tested. The results indicate that the R2 value between the model-estimated and measured values is 0.82 in the PRD region, and the model attains a high estimation accuracy. Moreover, the estimation accuracy of the model exhibits notable temporal and spatial heterogeneity. This study, to a certain extent, mitigates the shortcomings of traditional ground PM2.5 concentration monitoring methods with a high cost and low spatial resolution and complements satellite remote sensing technology. This study extends the use of LJ1-01 nighttime light remote sensing images to estimate nighttime PM2.5 concentrations. This yields a certain practical value and potential in nighttime ground PM2.5 concentration inversion.

Evidence of air quality data misreporting in China: An impulse indicator saturation model comparison of local government-reported and U.S. embassy-reported PM2.5 concentrations (2015-2017)

This paper analyzes hourly PM2.5 measurements from government-controlled and U.S. embassy-controlled monitoring stations in five Chinese cities between January 2015 and June 2017. We compare the two datasets with an impulse indicator saturation technique that identifies hours when the relation between Chinese and U.S. reported data diverges in a statistically significant fashion. These temporary divergences, or impulses, are 1) More frequent than expected by random chance; 2) More positive than expected by random chance; and 3) More likely to occur during hours when air pollution concentrations are high. In other words, relative to U.S.-controlled monitoring stations, government-controlled stations systematically under-report pollution levels when local air quality is poor. These results contrast with the findings of other recent studies, which argue that Chinese air quality data misreporting ended after a series of policy reforms beginning in 2012. Our findings provide evidence that local government misreporting did not end after 2012, but instead continued in a different manner. These results suggest that Chinese air quality data, while still useful, should not be taken entirely at face value.

Impacts of discriminated PM2.5 on global under-five and maternal mortality

Globally, it was estimated that maternal and under-five deaths were high in low-income countries than that of high-income countries. Most studies, however, have focused only on the clinical causes of maternal and under-five deaths, and yet there could be other factors such as ambient particulate matter (PM). The current global estimates indicate that exposure to ambient PM_2.5 (with ≤ 2.5 microns aerodynamic diameter) has caused about 7 million deaths and over 100 million disability-adjusted life-years. There are also several health risks that have been linked PM_2.5, including mortality, both regionally and globally; however, PM_2.5 is a mixture of many compounds from various sources. Globally, there is little evidence of the health effects of various types of PM_2.5, which may uniquely contribute to the global burden of disease. Currently, only two studies had estimated the effects of discriminated ambient PM_2.5, that is, anthropogenic, biomass and dust, on under-five and maternal mortality using satellite measurements, and this study found a positive association in Africa and Asia. However, the study area was conducted in only one region and may not reflect the spatial variations throughout the world. Therefore, in this study, we discriminated different ambient PM_2.5 and estimated the effects on a global scale. Using the generalized linear mixed-effects model (GLMM) with a random-effects model, we found that biomass PM_2.5 was associated with an 8.9% (95% confidence interval [CI] 4.1-13.9%) increased risk of under-five deaths, while dust PM_2.5 was marginally associated with 9.5% of under-five deaths. Nevertheless, our study found no association between PM_2.5 type and global maternal deaths. This result may be because the majority of maternal deaths could be associated with preventable deaths that would require clinical interventions. Identification of the mortality-related types of ambient PM_2.5 can enable the development of a focused intervention strategy of placing appropriate preventive measures for reducing the generation of source-specific PM_2.5 and subsequently diminishing PM_2.5-related mortality.

Associations between individual perceptions of PM2.5 pollution and pulmonary function in Chinese middle-aged and elderly residents

Background

PM_2.5 pollution has become a major public health concern in urban China. Understanding the residents’ individual perceptions toward haze pollution is critical for policymaking and risk communication. However, the perceptions of middle-aged and elderly residents, who particularly vulnerable to haze pollution, are poorly understood. In this study, we aimed to explore their risk perceptions of haze pollution and investigate its relationship with health status and pulmonary function parameters.

Methods

A cross-sectional study of 400 randomly sampled individuals (aged 40 to 90 years) was conducted in Wuxi, a typical PM_2.5-polluted city in Jiangsu Province, China (during 2015–2017, daily average concentration of PM_2.5 was 52.7 μg/m³). Each participant’s demographic and health information, individual perception and pulmonary function outcomes were collected to explore the relationships between perception factors and personal characteristics and pulmonary function parameters, using linear models.

Results

We found that the mean values for controllability (5 ± 2.8) and dread of risk to oneself (levels of fear for haze-related harm to oneself) (6.9 ± 2.5) were the lowest and the highest values, respectively, in our study. Education and average family income were positively related with all individual perception factors, while age was negatively associated. A history of respiratory disease was positively associated with all individual perception factors except controllability. Significant positive associations were observed between PEF (coefficients ranged from 0.18 to 0.22) and FEF75% (coefficients ranged from 0.18 to 0.29) with a variety of individual perception factors.

Conclusions

There were a lack of concern and knowledge, weak self-protection consciousness and a strong dread of PM_2.5 pollution among the middle-aged and elderly residents in Wuxi. Their individual perceptions were associated with age, education levels, average family income, history of respiratory disease, PEF and FEF75%. Our findings may help policymakers develop effective policies and communication strategies to mitigate the hazards of haze among older residents.

Short-Term Impact of Traffic-Related Particulate Matter and Noise Exposure on Cardiac Function

Exposure to traffic-related air pollution and noise exposure contributes to detrimental effects on cardiac function, but the underlying short-term effects related to their simultaneous personal exposure remain uncertain. The aim is to assess the impact of total inhaled dose of particulate matter and total noise exposure on the variations of electrocardiogram (ECG) parameters between pre-cycling and post-cycling periods. Mid-June 2019, we collected four participants' personal exposure data related to traffic-related noise and particulate matter (PM_2.5 and PM₁₀) as well as ECG parameters. Several Bayesian linear models were built to examine a potential association between air pollutants and noise exposure and ECG parameters: heart rate (HR), standard deviation of the normal-to-normal intervals (SDNN), percentage of successive RR intervals that differ by more than 50 ms (pNN50), root mean square of successive RR interval differences (rMSSD), low-frequency power (LF), high-frequency power (HF), and ratio of low- to high-frequency power (LF/HF). We analyzed in total 255 5-min segments of RR intervals. We observed that per 1 µg increase in cumulative inhaled dose of PM_2.5 was associated with 0.48 (95% CI: 0.22; 15.61) increase in variation of the heart rate, while one percent of total noise dose was associated with 0.49 (95% CI: 0.17; 0.83) increase in variation of heart rate between corresponding periods. Personal noise exposure was no longer significant once the PM_2.5 was introduced in the whole model, whilst coefficients of the latter that were significant previously remained unchanged. Short-term exposure to traffic-related air and noise pollution did not, however, have an impact on heart rate variability.

Interventions to reduce ambient particulate matter air pollution and their effect on health

BACKGROUND

Ambient air pollution is associated with a large burden of disease in both high-income countries (HICs) and low- and middle-income countries (LMICs). To date, no systematic review has assessed the effectiveness of interventions aiming to reduce ambient air pollution.

OBJECTIVES

To assess the effectiveness of interventions to reduce ambient particulate matter air pollution in reducing pollutant concentrations and improving associated health outcomes.

SEARCH METHODS

We searched a range of electronic databases with diverse focuses, including health and biomedical research (CENTRAL, Cochrane Public Health Group Specialised Register, MEDLINE, Embase, PsycINFO), multidisciplinary research (Scopus, Science Citation Index), social sciences (Social Science Citation Index), urban planning and environment (Greenfile), and LMICs (Global Health Library regional indexes, WHOLIS). Additionally, we searched grey literature databases, multiple online trial registries, references of included studies and the contents of relevant journals in an attempt to identify unpublished and ongoing studies, and studies not identified by our search strategy. The final search date for all databases was 31 August 2016.

SELECTION CRITERIA

Eligible for inclusion were randomized and cluster randomized controlled trials, as well as several non-randomized study designs, including controlled interrupted time-series studies (cITS-EPOC), interrupted time-series studies adhering to EPOC standards (ITS-EPOC), interrupted time-series studies not adhering to EPOC standards (ITS), controlled before-after studies adhering to EPOC standards (CBA-EPOC), and controlled before-after studies not adhering to EPOC standards (CBA); these were classified as main studies. Additionally, we included uncontrolled before-after studies (UBA) as supporting studies. We included studies that evaluated interventions to reduce ambient air pollution from industrial, residential, vehicular and multiple sources, with respect to their effect on mortality, morbidity and several air pollutant concentrations. We did not restrict studies based on the population, setting or comparison.

DATA COLLECTION AND ANALYSIS

After a calibration exercise among the author team, two authors independently assessed studies for inclusion, extracted data and assessed risk of bias. We conducted data extraction, risk of bias assessment and evidence synthesis only for main studies; we mapped supporting studies with regard to the types of intervention and setting. To assess risk of bias, we used the Graphic Appraisal Tool for Epidemiological studies (GATE) for correlation studies, as modified and employed by the Centre for Public Health Excellence at the UK National Institute for Health and Care Excellence (NICE). For each intervention category, i.e. those targeting industrial, residential, vehicular and multiple sources, we synthesized evidence narratively, as well as graphically using harvest plots.

MAIN RESULTS

We included 42 main studies assessing 38 unique interventions. These were heterogeneous with respect to setting; interventions were implemented in countries across the world, but most (79%) were implemented in HICs, with the remaining scattered across LMICs. Most interventions (76%) were implemented in urban or community settings.We identified a heterogeneous mix of interventions, including those aiming to address industrial (n = 5), residential (n = 7), vehicular (n = 22), and multiple sources (n = 4). Some specific interventions, such as low emission zones and stove exchanges, were assessed by several studies, whereas others, such as a wood burning ban, were only assessed by a single study.Most studies assessing health and air quality outcomes used routine monitoring data. Studies assessing health outcomes mostly investigated effects in the general population, while few studies assessed specific subgroups such as infants, children and the elderly. No identified studies assessed unintended or adverse effects.The judgements regarding the risk of bias of studies were mixed. Regarding health outcomes, we appraised eight studies (47%) as having no substantial risk of bias concerns, five studies (29%) as having some risk of bias concerns, and four studies (24%) as having serious risk of bias concerns. Regarding air quality outcomes, we judged 11 studies (31%) as having no substantial risk of bias concerns, 16 studies (46%) as having some risk of bias concerns, and eight studies (23%) as having serious risk of bias concerns.The evidence base, comprising non-randomized studies only, was of low or very low certainty for all intervention categories and primary outcomes. The narrative and graphical synthesis showed that evidence for effectiveness was mixed across the four intervention categories. For interventions targeting industrial, residential and multiple sources, a similar pattern emerged for both health and air quality outcomes, with essentially all studies observing either no clear association in either direction or a significant association favouring the intervention. The evidence base for interventions targeting vehicular sources was more heterogeneous, as a small number of studies did observe a significant association favouring the control. Overall, however, the evidence suggests that the assessed interventions do not worsen air quality or health.

AUTHORS' CONCLUSIONS

Given the heterogeneity across interventions, outcomes, and methods, it was difficult to derive overall conclusions regarding the effectiveness of interventions in terms of improved air quality or health. Most included studies observed either no significant association in either direction or an association favouring the intervention, with little evidence that the assessed interventions might be harmful. The evidence base highlights the challenges related to establishing a causal relationship between specific air pollution interventions and outcomes. In light of these challenges, the results on effectiveness should be interpreted with caution; it is important to emphasize that lack of evidence of an association is not equivalent to evidence of no association.We identified limited evidence for several world regions, notably Africa, the Middle East, Eastern Europe, Central Asia and Southeast Asia; decision-makers should prioritize the development and implementation of interventions in these settings. In the future, as new policies are introduced, decision-makers should consider a built-in evaluation component, which could facilitate more systematic and comprehensive evaluations. These could assess effectiveness, but also aspects of feasibility, fidelity and acceptability.The production of higher quality and more uniform evidence would be helpful in informing decisions. Researchers should strive to sufficiently account for confounding, assess the impact of methodological decisions through the conduct and communication of sensitivity analyses, and improve the reporting of methods, and other aspects of the study, most importantly the description of the intervention and the context in which it is implemented.

Characteristics and origins of air pollutants in Wuhan, China, based on observations and hybrid receptor models

To identify the characteristics of air pollutants and factors attributing to the formation of haze in Wuhan, this study analyzed the hourly observations of air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO) from March 1, 2013, to February 28, 2014, and used hybrid receptor models for a case study. The results showed that the annual average concentrations for PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO during the whole period were 89.6 μg m^-3, 134.9 μg m^-3, 54.9 μg m^-3, 32.4 μg m^-3, 62.3 μg m^-3, and 1.1 mg m^-3, respectively. The monthly variations revealed that the peak values of PM_2.5, PM₁₀, NO₂, SO₂, and CO occurred in December because of increased local emissions and severe weather conditions, while the lowest values occurred in July mainly due to larger precipitation. The maximum O₃ concentrations occurred in warm seasons from May to August, which may be partly due to the high temperature and solar radiation. Diurnal analysis showed that hourly PM_2.5, PM₁₀, NO₂, and CO concentrations had two ascending stages accompanying by the two traffic peaks. However, the O₃ concentration variations were different with the highest concentration in the afternoon. A case study utilizing hybrid receptor models showed the significant impact of regional transport on the haze formation in Wuhan and revealed that the mainly potential polluted sources were located in the north and south of Wuhan, such as Baoding and Handan in Hebei province, and Changsha in Hunan province.

IMPLICATIONS

Wuhan city requires a 5% reduction of the annual mean of PM_2.5 concentration by the end of 2017. In order to accomplish this goal, Wuhan has adopted some measures to improve its air quality. This work has determined the main pollution sources that affect the formation of haze in Wuhan by transport. We showed that apart from the local emissions, north and south of Wuhan were the potential sources contributing to the high PM_2.5 concentrations in Wuhan, such as Baoding and Handan in Hebei province, Zhumadian and Jiaozuo in Henan province, and Changsha and Zhuzhou in Hunan province.

Fine Particulate Matter Concentrations in Urban Chinese Cities, 2005-2016: A Systematic Review

Background: Particulate matter pollution has become a growing health concern over the past few decades globally. The problem is especially evident in China, where particulate matter levels prior to 2013 are publically unavailable. We conducted a systematic review of scientific literature that reported fine particulate matter (PM_2.5) concentrations in different regions of China from 2005 to 2016. Methods: We searched for English articles in PubMed and Embase and for Chinese articles in the China National Knowledge Infrastructure (CNKI). We evaluated the studies overall and categorized the collected data into six geographical regions and three economic regions. Results: The mean (SD) PM_2.5 concentration, weighted by the number of sampling days, was 60.64 (33.27) μg/m³ for all geographic regions and 71.99 (30.20) μg/m³ for all economic regions. A one-way ANOVA shows statistically significant differences in PM_2.5 concentrations between the various geographic regions (F = 14.91, p < 0.0001) and the three economic regions (F = 4.55, p = 0.01). Conclusions: This review identifies quantifiable differences in fine particulate matter concentrations across regions of China. The highest levels of fine particulate matter were found in the northern and northwestern regions and especially Beijing. The high percentage of data points exceeding current federal regulation standards suggests that fine particulate matter pollution remains a huge problem for China. As pre-2013 emissions data remain largely unavailable, we hope that the data aggregated from this systematic review can be incorporated into current and future models for more accurate historical PM_2.5 estimates.

Levels and Sources of PAHs in Air-borne PM2.5 of Hefei City, China

This work studied the concentrations and sources of polycyclic aromatic hydrocarbons (PAHs) in air-borne particulate matter of Hefei, China. Samples of PM_2.5 were collected daily at two sites during May, 2014, and January, 2015. The average daily concentration of PM_2.5 was 96.88 µg m^-3, which is higher than the 2012 China Ambient Air Quality Standard (GB3095-2012 24-h grade II) of 75 µg m^-3. The concentrations of 16 EPA priority polycyclic aromatic hydrocarbons (PAHs) were analyzed by gas chromatography-mass spectrometry. The PM_2.5-bound PAH concentrations ranged from 4.92 to 71.00 ng m^-3 (mean = 21.34 ng m^-3), and exhibited obvious seasonal (31.38 ng m^-3 in winter and 14.05 ng m^-3 in summer) and spatial variability (27.23 ng m^-3at site ME and 18.20 ng m^-3 at site MS). Meteorological conditions such as ambient temperature, wind speed and humidity had influences on the concentrations of PAHs. As an index for PAH carcinogenicity, the annual average concentration of benzo(a)pyrene ranged from 0.46 to 2.31 ng m^-3, with a mean of 1.15 ng m^-3. This mean was lower than the China Ambient Air Quality Standard (GB3095-2012) of 2.5 ng m^-3. The diagnostic PAH ratios and principal component analysis (PCA) suggested that combustion of coal and vehicle emissions were the main sources of PAHs in PM_2.5.