Ambient air pollution of particles and gas pollutants, and the predicted health risks from long-term exposure to PM2.5 in Zhejiang province, China

Specific analysis of PM2.5-attributed disease burden in typical areas of Northwest China

Background

Frequent air pollution events in Northwest China pose a serious threat to human health. However, there is a lack of specific differences assessment in PM2.5-related disease burden. Therefore, we aimed to estimate the PM2.5-related premature deaths and health economic losses in this typical northwest region, taking into account disease-specific, age-specific, and region-specific factors.

Methods

We utilized the WRF-Chem model to simulate and analyze the characteristics and exposure levels of PM2.5 pollution in Gansu Province, a typical region of Northwest China. Subsequently, we estimated the premature mortality and health economic losses associated with PM2.5 by combining the Global Exposure Mortality Model (GEMM) and the Value of a Statistical Life (VSL).

Results

The results suggested that the PM2.5 concentrations in Gansu Province in 2019 varied spatially, with a decrease from north to south. The number of non-accidental deaths attributable to PM2.5 pollution was estimated to be 14,224 (95% CI: 11,716-16,689), accounting for 8.6% of the total number of deaths. The PM2.5-related health economic loss amounted to 28.66 (95% CI: 23.61-33.63) billion yuan, equivalent to 3.3% of the regional gross domestic product (GDP) in 2019. Ischemic heart disease (IHD) and stroke were the leading causes of PM2.5-attributed deaths, contributing to 50.6% of the total. Older adult individuals aged 60 and above accounted for over 80% of all age-related disease deaths. Lanzhou had a higher number of attributable deaths and health economic losses compared to other regions. Although the number of PM2.5-attributed deaths was lower in the Hexi Corridor region, the per capita health economic loss was higher.

Conclusion

Gansu Province exhibits distinct regional characteristics in terms of PM2.5 pollution as well as disease- and age-specific health burdens. This highlights the significance of implementing tailored measures that are specific to local conditions to mitigate the health risks and economic ramifications associated with PM2.5 pollution.

Interpreting Highly Variable Indoor PM2.5 in Rural North China Using Machine Learning

Household air pollution associated with solid fuel use is a long-standing public concern. The global population mainly using solid fuels for cooking remains large. Besides cooking, large amounts of coal and biomass fuels are burned for space heating during cold seasons in many regions. In this study, a wintertime multiple-region field campaign was carried out in north China to evaluate indoor PM2.5 variations. With hourly resolved data from ∼1600 households, key influencing factors of indoor PM2.5 were identified from a machine learning approach, and a random forest regression (RFR) model was further developed to quantitatively assess the impacts of household energy transition on indoor PM2.5. The indoor PM2.5 concentration averaged at 120 μg/m3 but ranged from 16 to ∼400 μg/m3. Indoor PM2.5 was ∼60% lower in families using clean heating approaches compared to those burning traditional coal or biomass fuels. The RFR model had a good performance (R2 = 0.85), and the interpretation was consistent with the field observation. A transition to clean coals or biomass pellets can reduce indoor PM2.5 by 20%, and further switching to clean modern energies would reduce it an additional 30%, suggesting many significant benefits in promoting clean transitions in household heating activities.

Multistation collaborative prediction of air pollutants based on the CNN-BiLSTM model

The development of industry has led to serious air pollution problems. It is very important to establish high-precision and high-performance air quality prediction models and take corresponding control measures. In this paper, based on 4 years of air quality and meteorological data from Tianjin, China, the relationships between various meteorological factors and air pollutant concentrations are analyzed. A hybrid deep learning model consisting of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) is proposed to predict pollutant concentrations. In addition, a Bayesian optimization algorithm is applied to obtain the optimal combination of hyperparameters for the proposed deep learning model, which enhances the generalization ability of the model. Furthermore, based on air quality data from multiple stations in the region, a multistation collaborative prediction method is designed, and the concept of a strongly correlated station (SCS) is defined. The predictive model is modified using the idea of SCS and is used to predict the pollutant concentration in Tianjin. The coefficient of determination R2 of PM2.5, PM10, SO2, NO2, CO, and O3 are 0.89, 0.84, 0.69, 0.83, 0.92, and 0.84, respectively. The results show that our model is capable of dealing with air pollutant prediction with satisfactory accuracy.

Surface ozone pollution in China: Trends, exposure risks, and drivers

Introduction

Within the context of the yearly improvement of particulate matter (PM) pollution in Chinese cities, Surface ozone (O₃) concentrations are increasing instead of decreasing and are becoming the second most important air pollutant after PM. Long-term exposure to high concentrations of O₃ can have adverse effects on human health. In-depth investigation of the spatiotemporal patterns, exposure risks, and drivers of O₃ is relevant for assessing the future health burden of O₃ pollution and implementing air pollution control policies in China.

Methods

Based on high-resolution O₃ concentration reanalysis data, we investigated the spatial and temporal patterns, population exposure risks, and dominant drivers of O₃ pollution in China from 2013 to 2018 utilizing trend analysis methods, spatial clustering models, exposure-response functions, and multi-scale geographically weighted regression models (MGWR).

Results

The results show that the annual average O₃ concentration in China increased significantly at a rate of 1.84 μg/m³/year from 2013 to 2018 (160 μg/m³) in China increased from 1.2% in 2013 to 28.9% in 2018, and over 20,000 people suffered premature death from respiratory diseases attributed to O₃ exposure each year. Thus, the sustained increase in O₃ concentrations in China is an important factor contributing to the increasing threat to human health. Furthermore, the results of spatial regression models indicate that population, the share of secondary industry in GDP, NOx emissions, temperature, average wind speed, and relative humidity are important determinants of O₃ concentration variation and significant spatial differences are observed.

Discussion

The spatial differences of drivers result in the spatial heterogeneity of O₃ concentration and exposure risks in China. Therefore, the O₃ control policies adapted to various regions should be formulated in the future O₃ regulation process in China.

Ambient NO2 is associated with Streptococcus pneumoniae-induced pneumonia in children and increases the minimum inhibitory concentration of penicillin

The present study aimed to assess the correlation between ambient air pollutants and Streptococcus pneumoniae (S. pneumoniae)-induced pneumonia in children and retrospectively reviewed the daily data regarding S. pneumoniae from children with pneumonia in a tertiary hospital of Hangzhou City, between January 1st, 2018, and December 31st, 2018. The excess risk (ER) of NO₂ with regard to the daily number of S. pneumoniae isolates obtained from the respiratory tract specimens of children with pneumonia was 13.31% (95% confidence interval [CI]: 3.12-24.51%, P = 0.010) in the single-pollutant model. An increase of 10 μg/m³ in NO₂ exposure was associated with a 23.30% increased risk for the acquisition of S. pneumoniae-induced pneumonia in children (95% CI: 2.02-49.02%; P = 0.031) according to the multi-pollutant model. The ER of NO₂ with regard to the daily number of S. pneumoniae isolates (1 μg/ml ≤ minimum inhibitory concentration (MIC) to penicillin ≤ 2 μg/ml) obtained from the respiratory tract specimens of children with pneumonia was 15.80% (95% CI: 2.02-31.45%; P = 0.024) in the single-pollutant model. According to the multi-pollutant model, the ER of NO₂ with regard to the daily number of S. pneumoniae isolates (1 μg/ml ≤ MIC to penicillin ≤ 2 μg/ml) obtained from the respiratory tract specimens of children with pneumonia was 37.09% (95% CI: 5.70-77.81%; P = 0.018). In conclusion, ambient NO₂ is associated with S. pneumoniae-induced pneumonia in children. More importantly, NO₂ exposure is associated with the increased MICs of penicillin against S. pneumoniae from children with pneumonia.

Relationship between Air Pollution and Hospital Admissions for Chronic Obstructive Pulmonary Disease in Changchun, China: A Season-Stratified Case-Cross Study

Background

This study aimed to explore the relationship between air pollution and hospital admissions for COPD in Changchun, a northeast city of China, in different seasons.

Methods

The data on a total of 1,733 hospitalized patients living in Changchun with acute exacerbation of COPD from September 2013 to April 2018 were collected from a comprehensive 3A hospital of Changchun. Daily average concentrations of PM2.5, PM10, SO₂, NO₂, CO, and O₃ were collected from the Department of Ecology and Environment of Jilin Province. The conditional logistic regression model was adopted to analyze the effect of air pollutant concentration on the number of hospitalized patients with COPD in different seasons.

Results

The maximum OR value for most air pollutants emitted in spring was on lag day 4, in summer and autumn on lag day 3, and in winter on lag day 2. In spring, SO₂ and NO₂ were entered into the regression equation, and the OR (95%CI) was 0.992 (0.986-0.998) and 1.009 (1.002-1.017); in autumn, PM2.5, PM10, and SO₂ were entered into the regression equation, and the OR (95%CI) was 1.005 (1.000-1.011), 0.995 (0.991-1.000), and 1.006 (1.001-1.011), respectively; and in winter, PM2.5 and PM10 were entered into the regression equation, and the OR (95%CI) was 1.008 (1.002-1.015) and 0.994 (0.988-0.999), respectively.

Conclusion

The relationship between air pollution and hospital admission for COPD in Northeast China varies with different seasons. In spring, NO₂ is likely to be the major risk factor for hospital admissions for COPD; in autumn, PM2.5 and SO₂ are the major risk factors; and in winter, PM2.5 is the major risk factor.

Short-term nitrogen dioxide exposure is associated with the spread of S. pyogenes-induced vulvovaginitis in prepubertal girls in Hangzhou, China

As a cause of vulvovaginitis in prepubertal girls, Streptococcus pyogenes (S. pyogenes) is commonly isolated from vaginal introitus swabs. Studies have identified several risk factors, but have not focused on the correlation between ambient air pollutants and S. pyogenes-induced vulvovaginitis in prepubertal girls. This study was conducted to determine whether ambient air pollutants were associated with S. pyogenes-induced vulvovaginitis in prepubertal girls. Daily data about S. pyogenes-induced vulvovaginitis in prepubertal girls from the outpatient department of Children's Hospital at the Zhejiang University School of Medicine in Hangzhou City between January 1, 2015, and December 31, 2018, were retrospectively reviewed. Ambient air pollutants in Hangzhou were measured daily. A generalized additive model (GAM) was utilized to assess the associations between daily air pollutants and S. pyogenes isolates obtained from vaginal introitus swabs of prepubertal girls. The mean daily concentration of nitrogen dioxide (NO₂) in Hangzhou City during the study period was 44.6 μg/m³ (25th-75th percentiles, 32.0-56.0 μg/m³). The GAM showed that the largest estimate effects in S. pyogenes-induced vulvovaginitis in prepubertal girls were found in NO₂ with a moving (accumulative) average on day 3. The excess risk of NO₂ in terms of the daily number of S. pyogenes isolates obtained from the vaginal introitus swabs was 14.91% (95% confidence interval [CI]: 4.85-25.94%) in the single-pollutant model. The multipollutant model revealed that an increase of 10 μg/m³ in NO₂ exposure was associated with an 18.33% increased risk for acquiring S. pyogenes-induced vulvovaginitis in prepubertal girls (95% CI: 1.21-38.35%; P < 0.05). In conclusion, short-term NO₂ exposure was strongly associated with the spread of S. pyogenes-induced vulvovaginitis in prepubertal girls.

Air pollutants and outpatient visits for influenza-like illness in Beijing, China

Background

Air pollution leads to many adverse health conditions, mainly manifested by respiratory or cardiac symptoms. Previous studies are limited as to whether air pollutants were associated to influenza-like illness (ILI). This study aimed to explore the association between air pollutants and outpatient visits for ILI, especially during an outbreak of influenza.

Methods

Daily counts of hospital visits for ILI were obtained from Peking University Third Hospital between January 1, 2015, and March 31, 2018. A generalized additive Poisson model was applied to examine the associations between air pollutants concentrations and daily outpatient visits for ILI when adjusted for the meteorological parameters.

Results

There were 35862 outpatient visits at the fever clinic for ILI cases. Air quality index (AQI), PM_2.5, PM₁₀, CO and O₃ on lag0 days, as well as nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) on lag1 days, were significantly associated with an increased risk of outpatient visits for ILI from January 2015 to November 2017. From December 2017 to March 2018, on lag0 days, air pollutants PM_2.5 [risk ratio (RR) = 0.971, 95% CI: 0.963-0.979], SO₂ (RR = 0.892, 95% CI: 0.840–0.948) and CO (RR = 0.306, 95% CI: 0.153–0.612) were significantly associated with a decreased risk of outpatient visits for ILI. Interestingly, on the lag2 days, all the pollutants were significantly associated with a reduced risk of outpatient visits for ILI except for O₃. We did not observe the linear correlations between the outpatient visits for ILI and any of air pollutants, which were instead associated via a curvilinear relationship.

Conclusions

We found that the air pollutants may be associated with an increased risk of outpatient visits for ILI during the non-outbreak period and with a decreased risk during the outbreak period, which may be linked with the use of disposable face masks and the change of outdoor activities. These findings expand the current knowledge of ILI outpatient visits correlated with air pollutants during an influenza pandemic.

Internal metal(loid)s are potentially involved in the association between ambient fine particulate matter and blood pressure: A repeated-measurement study in north China

The effects of ambient fine particulate matter (PM_2.5) exposure on blood pressure have been widely reported. However, there remains uncertainty regarding the underlying roles of particulate matter components. We aimed to investigate the association between ambient PM_2.5 exposure and blood pressure, as well as the potential effects of trace metal(loid)s, in a repeated-measurement study that enrolled women of childbearing age. Our study included 35 participants from Hebei Province, China, each of whom was visited for five times. During each visit, we conducted questionnaire surveys, measured blood pressure, and collected blood. The daily PM_2.5 exposure of participants was estimated according to their residential addresses using a spatiotemporal model that combined monitoring data with satellite measurements and chemical-transport model simulations. This model was used to calculate average PM_2.5 concentrations in 1, 3, 7, 15, 30, and 60 days prior to each visit. Serum concentrations of various trace metal(loid)s were measured. A linear mixed-effects model was used to investigate associations among study variables. Overall, the mean (standard deviation) 60 days PM_2.5 concentration over all five visits was 108.1(43.3) μg/m³. PM_2.5 concentration was positively associated with both systolic and diastolic blood pressures. Likewise, ambient PM_2.5 concentration was positively associated with serum concentrations of manganese and arsenic, and negatively associated with serum concentrations of nickel, tin, and chromium. Only the serum concentration of molybdenum was negatively associated with systolic blood pressure. We concluded that ambient PM_2.5 exposure may contribute to elevated blood pressure, potentially by interfering with internal intake of various metal(loid)s in the human body.

Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia: An Area of the Po Valley

Spatio-temporal Bayesian disease mapping is the branch of spatial epidemiology interested in providing valuable risk estimates in certain geographical regions using administrative areas as statistical units. The aim of the present paper is to describe spatio-temporal distribution of cardiovascular mortality in the Province of Pavia in 2010 through 2015 and assess its association with environmental pollution exposure. To produce reliable risk estimates, eight different models (hierarchical log-linear model) have been assessed: temporal parametric trend components were included together with some random effects that allowed the accounting of spatial structure of the region. The Bayesian approach allowed the borrowing information effect, including simpler model results in the more complex setting. To compare these models, Watanabe–Akaike Information Criteria (WAIC) and Leave One Out Information Criteria (LOOIC) were applied. In the modelling phase, the relationship between the disease risk and pollutants exposure (PM2.5) accounting for the urbanisation level of each geographical unit showed a strong significant effect of the pollutant exposure (OR = 1.075 and posterior probability, or PP, >0.999, equivalent to p < 0.001). A high-risk cluster of Cardiovascular mortality in the Lomellina subareas in the studied window was identified.

Association between ambient air pollution and laryngeal neoplasms incidence in twelve major Chinese cities, 2006-2013

Epidemiological evidence has suggested that ambient air pollution is an increasingly important risk factor for respiratory diseases without assessing its influence on laryngeal neoplasms incidence in China. We constructed two-way fixed effect models and Poisson regression models to explore the effects of ambient air pollutants including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and particulate matter less than or equal to 10 μm in aerodynamic diameter (PM₁₀) on incidence of laryngeal neoplasms in twelve major cities in China over the period 2006-2013. The annual average concentration for PM₁₀, SO₂, and NO₂ was 107.22 μg/m³, 44.07 μg/m³, and 46.71 μg/m³ with standard deviations of 24.84 μg/m³, 13.68 μg/m³, and 9.19 μg/m³, respectively. We observed that ambient air pollutants were significantly positively correlated with the incidence of laryngeal neoplasms, especially for NO₂. The relative risks of overall incidence of laryngeal neoplasms in the current period were 1.20, 1.04, and 1.00 for NO₂, SO₂, and PM₁₀, with 95% confidence intervals (CIs) of 1.01-1.43, 0.93-1.16, and 0.96-1.05, respectively. Moreover, this deleterious impact was stronger in the male than in the female, likely due to genetic predisposition caused by longer exposure to more serious air pollution for men. Our findings complement the epidemiological evidence of laryngeal neoplasms due to ambient air pollution and reinforce the necessity of policy efforts to control the noxious air pollution emissions.

Household air pollution from cooking and heating and its impacts on blood pressure in residents living in rural cave dwellings in Loess Plateau of China

Cave dwelling is an ancient and unique type of residence in the Loess Plateau of Northern China, where the economics are less-developed. The majority of the local dwellers rely on traditional solid fuels for cooking and heating, which can emit large amounts of particles into both indoor and outdoor environments. In this study, we measured the real-time household concentrations of PM_2.5 and explored the association between personal daily PM_2.5 exposure and blood pressure (BP). Cooking and heating activities with different energies made a great variation in the household PM_2.5 air pollution, and residents using biomass had the highest personal PM_2.5 exposure. Temperature and relative humidity are both significantly linear correlated with household PM_2.5 air pollution. Besides, systolic blood pressure (SBP) was demonstrated to be positively associated with personal PM_2.5 exposure: with each 10-μg/m³ incremental PM_2.5 concentration when controlling all the other factors, SBP will increase by 0.36 mmHg (95% confident interval (CI) 0.05-0.0.77 mmHg). If solid fuels could be replaced with clean energies, personal PM_2.5 exposure and SBP would reduce by more than 21% and 3.7%, respectively, calling for efficient intervention programs to mitigate household air pollution of cave dwellings and protect health of those residents.

Investigating the Relationship between the Industrial Structure and Atmospheric Environment by an Integrated System: A Case Study of Zhejiang, China

Under the dual pressure of industrial structure upgrade and atmospheric environment improvement, China, in a transition period, is facing the challenge of coordinating the relationship between the industry and the environment system to promote the construction of a beautiful China. Based on system theory and coupling coordination model, the interaction analysis framework between industrial structure (IS) and atmospheric environment (AE) was constructed. An integrated system with 24 indicators was established by the pressure–state–response (PSR) model of IS and level–quality–innovation (LQI) model of AE. Then, we analyzed trends observed in coupling coordination degree (CCD) and dynamic coupling coordination degree (DCCD) for 11 cities in Zhejiang Province, China, using statistical panel data collected from 2006 to 2017. Conclusions were as follows: (1) the 11 cities’ comprehensive level of the IS system shows a trend of stable increase, yet the comprehensive level of AE demonstrated a trend of fluctuation and transition. There are significant spatial variations among cities; (2) The CCD analysis results found that Hangzhou, Ningbo, and Wenzhou take the lead in realizing the transformation from barely coordinated development to superior coordinated pattern, while other cities were still in the stage of barely coordinated development; (3) the DCCD phase of 11 cities can be roughly divided into three types: upgraded—utmost development type (only Hangzhou), stable—harmonious development type (Wenzhou, Lishui, and Zhoushan) and transitional—harmonious development type (the remaining seven cities). This means, for most cities, the contradiction between the transformation process of IS and the AE has become increasingly prominent and intensified. Finally, three necessary and sustainable strategies were proposed to environmental policy makers.

Maternal ambient air pollution exposure with spatial-temporal variations and preterm birth risk assessment during 2013-2017 in Zhejiang Province, China

Preterm birth (PTB) can give rise to significant neonatal morbidity and mortality, as well as children's long-term health defects. Many studies have illustrated the associations between ambient air pollution exposure during gestational periods and PTB risks, but most of them only focused on one single air pollutant, such as PM_2.5. In this population-based environmental-epidemiology study, we recruited 6275 pregnant mothers in Zhejiang Province, China, and evaluated their gestational exposures to various air pollutants during 2013-2017. Time-to-event logistic regressions were performed to estimate risk associations after adjusting all confounders, and Quasi-AQI model and PCA-GLM analysis were applied to resolve the collinearity issues in multi-pollutant regression models. It was found that gestational exposure to ambient air pollutants was significantly associated with the occurrence of PTB, and SO₂ was the largest contributor with a proportion of 29.4%. Three new variables, prime factor (a combination of PM_2.5, PM₁₀, SO₂, and NO₂), carbon factor (CO), and ozone factor (O₃), were generated by PCA integration, contributing 63.4%, 17.1%, and 19.5% to PTB risks, respectively. The first and third trimester was the most crucial exposure window, suggesting the pregnant mothers better to avoid severe air pollution exposures during these sensitive periods.

Sustainable Development of New Urbanization from the Perspective of Coordination: A New Complex System of Urbanization‒Technology Innovation and the Atmospheric Environment

Exploring the coordinated development of urbanization (U), technology innovation (T), and the atmospheric environment (A) is an important way to realize the sustainable development of new-type urbanization in China. Compared with existing research, we developed an integrated index system that accurately represents the overall effect of the three subsystems of UTA, and a new weight determination method, the structure entropy weight (SEW), was introduced. Then, we constructed a coordinated development index (CDI) of UTA to measure the level of sustainability of new-type urbanization. This study also analyzed trends observed in UTA for 11 cities in Zhejiang Province of China, using statistical panel data collected from 2006 to 2017. The results showed that: (1) urbanization efficiency, the benefits of technological innovation, and air quality weigh the most in the indicator systems, which indicates that they are key factors in the behavior of UTA. The subsystem scores of the 11 cities show regional differences to some extent. (2) Comparing the coordination level of UTA subsystems, we found that the order is: coordination degree of UT > coordination degree of UA > coordination degree of TA. This suggests that the atmospheric environment system improvement is an important strategic decision for sustainable urbanization in Zhejiang. (3) The UTACDI values of the 11 cities are not high enough, as the coordination is mainly low, basic, or good, while none of the cities reached the stage of excellent coordination. (4) Gray Model (1,1) revealed that the time taking to achieve excellent coordination varies for different cities. Hangzhou and Ningbo were predicted to reach the excellent coordination level in 2018. Other cities are predicted to take 2–4 years to adjust their urbanization strategies enough to be considered to have excellent coordination of their UTA system.