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Exploring sources of uncertainty in premature mortality estimates from fine particulate matter: the case of China
Atmospheric pollution from fine particulate matter (PM2.5) is one of the major concerns in China because of its widespread and harmful impacts on human health. In recent years, multiple studies have sought to estimate the premature mortality burden from exposure to PM2.5 to inform policy decisions. However, different modeling choices have led to a wide array of results, with significant discrepancies both in the total mortality burden and in the confidence intervals. Here, we present a new comprehensive assessment of PM2.5-related mortality for China, which includes quantification of the main sources of variability, as well as of age and province-specific premature mortality trends during 2015-2018. Our approach integrates PM2.5 observations from more than 1600 monitoring stations with the output of a high-resolution (8 km) regional simulation, to accurately estimate PM2.5 fields along with their uncertainty, which is generally neglected. We discuss the sensitivity of mortality estimates to the choice of the exposure-response functions (ERFs), by comparing the widely used integrated exposure response functions (IERs) to the recently developed Global Exposure Mortality Models (GEMMs). By propagating the uncertainty in baseline mortalities, PM2.5 and ERFs under a Monte Carlo framework, we show that the 95% confidence intervals of mortality estimates are considerably wider than previously reported. We thus highlight the need for more epidemiological studies to constrain ERFs and we argue that uncertainty related to PM2.5 estimate should be also incorporated in health impact assessment studies. Although the overall mortality burden remains vast in China (∼1.6 million premature deaths, according to GEMMs), our results suggest that 200 000 premature deaths were avoided and 195 billion US dollars were saved in 2018 compared to 2015, bolstering the mounting evidence about the effectiveness of China's air quality policies.
Journal Article
Clearer Skies Over China
China's carbon dioxide emissions now outstrip those of other countries and its domestic air quality is severely degraded, especially in urban areas. Its sheer size and its growing, fossil-fuel-powered economy mean that China's economic and environmental policy choices will have an outsized effect on the global environmental future. Over the last decade, China has pursued policies that target both fossil fuel use and atmospheric emissions, but these efforts have been substantially overwhelmed by the country's increasing energy demands. With a billion citizens still living on less than $4,000 per year, China's energy and environmental policies must be reconciled with the goals of maintaining economic growth and raising living standards. This book, a U.S.--Chinese collaboration of experts from Harvard and Tsinghua University, offers a groundbreaking integrated analysis of China's economy, emissions, air quality, public health, and agriculture. It first offers essential scientific context and accessible summaries of the book's policy findings; it then provides the underlying scientific and economic research. These studies suggest that China's recent sulfur controls achieved enormous environmental health benefits at unexpectedly low costs. They also indicate that judicious implementation of carbon taxes could reduce not only China's carbon emissions but also its air pollution more comprehensively than current single-pollutant policies, all at little cost to economic growth.
eBook
How protective is China's National Ambient Air Quality Standards on short-term PM2.5? Findings from blood pressure measurements of 1 million adults
Although short-term exposure to fine particulate matter (PM2.5) air pollution has been shown to induce elevated blood pressure (BP), limited evidence is available regarding the association between ambient PM2.5 and BP levels in nationwide China and how the association may change. This study sought to explore acute BP changes with exposure to PM2.5 at levels below China's current National Ambient Air Quality Standards (NAAQS). Based on a spatiotemporal study of over 1 million adults, we linked BP measurements to daily estimates of PM2.5 from multiple sources (i.e. in situ observations, satellite measurements of aerosol and numeric simulations of air quality model) after adjusting for several individual-level covariates and further conduced the below-criteria models by restricting the analyses within subsets of individuals with short-term PM2.5 exposure below 75 μg m−3 (i.e. NAAQS of 24 h PM2.5 in China). We further explored variations in BP-PM2.5 associations by pollution level and for different demographic groups. With full adjustments, a 10 μg m−3 increase in PM2.5 was statistically significantly associated with a 0.049 mmHg (95% confidence interval, CI: 0.041, 0.057) increase in systolic BP, a 0.022 mmHg (95% CI: 0.017, 0.027) increase in diastolic BP and a 0.77% (95% CI: 0.62%, 0.92%) increased risk of hypertension (HPN). For both BP and HPN, the exposure-response curves were linear, with no threshold effects, at the low-concentration ends and sublinear at the high-concentration ends. Below the current NAAQS, the associations in population level remained statistically significant and were even stronger. A 10 μg m−3 increase in below-NAAQS PM2.5 was associated with a 1.95% (95% CI: 1.44%, 2.47%) increased risk of HPN. Specific subpopulations were more susceptible to PM2.5 exposure. These findings can help support decisions by policymakers to revise related environmental regulations to protect public health.
Journal Article
How Effective are Air Pollution Control Policies in China? Evidence from 35 Cities Nationwide
In September 2013, China began to implement a series of policies in tackling severe air pollution. This paper aims to explore the diversity and effectiveness of its air pollution control policies at the city level. A city-level pollution control policy indicator is constructed for 35 Chinese cities during the last two air pollution control action periods from 2014 to 2017 and 2018 to 2020. Additionally, this paper employs the panel vector auto-regression model (PVAR) to estimate the impact of air pollution control policies on air pollution reduction. The empirical results show that, in terms of the two main air quality indicators, PM2.5 and PM10, China’s air pollution control policies have helped improve the air quality over the last several years. The study concludes that air quality improvement should depend on coordinated strategies for controlling various pollutants that involve the collaboration of government and industries.
Journal Article
Comparison of Willingness to Pay for Quality Air and Renewable Energy Considering Urban Living Experience
Currently, quality air and renewable energy are main concerns in protecting the environment. Comparing willingness to pay for quality air and renewable energy is rare in the existing literature. However, the public faces these issues simultaneously. In addition, population mobility under China’s household registration system, i.e., urban living experience, may affect the willingness to pay for environmental protection. Consequently, the difference between people’s willingness to pay for quality air and renewable energy in China is discussed. Binary logistic regression is adopted to analyze the correlation factors based on data from the China General Social Survey. The results show that willingness to pay is influenced by environmental attitudes, awareness of energy use, government responsibilities, age, household income level, and trust. The effects of urban living experience on willingness to pay weakens as age decreases. Thus, improving environmental awareness and specialized knowledge remain important means of promoting willingness to pay. Our findings can help marketers and policy designers develop balanced or targeted measures when taking joint actions.
Journal Article
Impact of air pollution on the burden of chronic respiratory diseases in China: time for urgent action
In China, where air pollution has become a major threat to public health, public awareness of the detrimental effects of air pollution on respiratory health is increasing—particularly in relation to haze days. Air pollutant emission levels in China remain substantially higher than are those in developed countries. Moreover, industry, traffic, and household biomass combustion have become major sources of air pollutant emissions, with substantial spatial and temporal variations. In this Review, we focus on the major constituents of air pollutants and their impacts on chronic respiratory diseases. We highlight targets for interventions and recommendations for pollution reduction through industrial upgrading, vehicle and fuel renovation, improvements in public transportation, lowering of personal exposure, mitigation of the direct effects of air pollution through healthy city development, intervention at population-based level (systematic health education, intensive and individualised intervention, pre-emptive measures, and rehabilitation), and improvement in air quality. The implementation of a national environmental protection policy has become urgent.
Journal Article
Enhanced Air Quality Prediction through Spatio-temporal Feature Sxtraction and Fusion: A Self-tuning Hybrid Approach with GCN and GRU
Accurate prediction of air quality change is essential for air pollution control and human daily mobility. Due to the strong spatial and temporal correlation of air quality changes, existing air quality prediction methods often face the problem of low prediction accuracy due to insufficient extraction of spatio-temporal features. In this paper, we proposed a self-tuning spatio-temporal neural network (ST2NN) to enhance air quality prediction. ST2NN model consisted of four modules. First, ST2NN model constructed a temporal feature extraction module and a spatial feature extraction module based on gated recurrent unit (GRU) and graph convolutional neural network (GCN), respectively, and the two feature extraction modules adopted a parallel structure, which could effectively extract the spatio-temporal features in data. Additionally, ST2NN model constructed a feature fusion module based on gating mechanism to delineate the contribution of spatio-temporal features to the predicted values. Further, ST2NN model constructed a Hyperband hyperparameter optimization module based on Hyperband optimization algorithm to automatically adjust the network hyperparameters. The structure of ST2NN model endowed it with excellent spatio-temporal feature extraction and parameter adaptability. ST2NN model was evaluated and compared with existing models, including convolutional long short-term memory neural network (ConvLSTM), GRU, combined convolutional neural network and long short-term memory neural network (CNN-LSTM), and GCN-LSTM for air quality index (AQI) prediction using data from twelve monitoring stations in Beijing, China. Across all four evaluation indexes, ST2NN model outperformed the comparative models, improving prediction accuracy by 0.51%-10.18% (measured using R2). From the experimental results, it can be seen that ST2NN model constructed from the perspective of spatio-temporal feature extraction has better prediction performance compared with the existing air quality prediction model, which provides a new method for air quality prediction and has certain application value.
Journal Article