Air quality improvement and health benefit of PM2.5 reduction from the coal cap policy in the Beijing-Tianjin-Hebei (BTH) region, China

Rebuilding high-quality near-surface ozone data based on the combination of WRF-Chem model with a machine learning method to better estimate its impact on crop yields in the Beijing-Tianjin-Hebei region from 2014 to 2019

In recent years, the problem of surface ozone pollution in China has been of great concern. According to observation data from monitoring stations, the concentration of near-surface ozone (O3) in China has gradually increased in recent years, and ozone concentration often exceeds the contaminant limit standard, especially in the Beijing-Tianjin-Hebei (BTH) region. High O3 concentration pollution will adversely affect crop growth, which can cause crop yield losses. Therefore, it is urgent to recognize the situation of ozone pollution in the BTH region and quantitatively evaluate the crop yield losses caused by ozone pollution to develop more effective pollution prevention and control policies. However, the monitoring of ozone concentration in China started relatively late compared with some developed countries, and currently, long-time series data covering the BTH region cannot be obtained, which makes it difficult to evaluate the impact of ozone on crop yield. Therefore, a new method (WRFC-XGB) was proposed in this study to establish a high-precision near-surface O3 concentration dataset covering the whole BTH region from 2014 to 2019 by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) machine learning algorithm. Through verification with ground observation station data, the results of WRFC-XGB are satisfactory, and R2 can reach 0.78-0.91. Compared with other algorithms, the accuracy of the near-surface ozone concentration dataset is greatly improved, which can be used to estimate the impact of surface ozone on crop yield. Based on this dataset, the yield loss of winter wheat, rice, and maize caused by O3 pollution was estimated by using the response equation of the relative yield and ozone dose index. The results showed that the total yield losses of winter wheat, rice and maize from 2014 to 2019 were 2659.21 million tons, 49.23 million tons and 1721.56 million tons due to ozone pollution in the BTH region, respectively, and the highest relative yield loss of crops caused by O3 pollution could be 29.37% during 2014-2019, which indicated that the impact of ozone pollution on crop yield cannot be ignored, and effective measures need to be developed to control ozone pollution, prevent crop production loss, and ensure people's food security.

Regional joint PM2.5-O3 control policy benefits further air quality improvement and human health protection in Beijing-Tianjin-Hebei and its surrounding areas

Beijing-Tianjin-Hebei and its surrounding areas (hereinafter referred to as "2+26" cities) are one of the most severe air pollution areas in China. The fine particulate matter (PM_2.5) and surface ozone (O₃) pollution have aroused a significant concern on the national scale. In this study, we analyzed the pollution characteristics of PM_2.5 and O₃ in "2+26" cities, and then estimated the health burden and economic loss before and after the implementation of the joint PM_2.5-O₃ control policy. During 2017-2019, PM_2.5 concentration reduced by 19% while the maximum daily 8 hr average (MDA8) O₃ stayed stable in "2+26" cities. Spatially, PM_2.5 pollution in the south-central area and O₃ pollution in the central region were more severe than anywhere else. With the reduction in PM_2.5 concentration, premature deaths from PM_2.5 decreased by 18% from 2017 to 2019. In contrast, premature deaths from O₃ increased by 5%. Noticeably, the huge potential health benefits can be gained after the implementation of a joint PM_2.5-O₃ control policy. The premature deaths attributed to PM_2.5 and O₃ would be reduced by 91.6% and 89.1%, and the avoidable economic loss would be 60.8 billion Chinese Yuan (CNY), and 68.4 billion CNY in 2035 compared with that in 2019, respectively. Therefore, it is of significance to implement the joint PM_2.5-O₃ control policy for improving public health and economic development.

An interpretable prediction of FCM driven by small samples for energy analysis based on air quality prediction

In order to achieve prevention and control of air pollution through energy consumption adjustment in advance, the paper proposes an Fuzzy Cognitive Map (FCM) of various energy resources affecting air quality, an incremental prediction algorithm of FCM and gradient descending method used to learn the FCM based on the small sample data on various energy consumptions and concentration of air pollutants. The FCM as an interpretable prediction method not only can predict future air quality more accurately, but also can analyze and interpret the affecting of various energy types on the future air quality. As the time delay of various energy consumptions affecting concentration of air pollutants, the quantitative time sequence influencing relationships (causality) in the FCM is mined directly from these data, and the air quality affected by various types of energy consumptions is predicted based on the FCM. Accordingly, the energy types affecting air pollution can be obtained for prior decision of energy consumption structure adjustment. The experimental results in Beijing-Tianjin-Hebei show that the FCM modeling is better than Support Vector Regression (SVR), Linear Regression (LR), Principal Component Analysis (PCA)-based forecasting, Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) methods in predicting air quality affected by energy resources, meanwhile according to the interpretable prediction results of the FCM, we obtain some interesting results and suggestions on energy consumption types in Beijing-Tianjin-Hebei regions in advance.Implications: At present, China's air pollution control has entered the deep-water area, and the biggest challenge is how to adjust the energy (consumption) structure. Therefore, this study completed the two important tasks: (1) driven by small sample data of energy consumptions, the paper provides an interpretable prediction model and method with better performance to achieve prevention and control of air pollution through energy consumption adjustment in advance; (2) according to the interpretable prediction results, the paper obtains some interesting results used to guide energy consumption adjustment in Beijing-Tianjin-Hebei regions. This study will provide beneficial suggestions and strategies for air pollution prevention and control in Beijing-Tianjin-Hebei, will help improve the air quality and energy consumption structure in Beijing-Tianjin-Hebei, and also can be extended to other regions.

The impact of the aerosol reduction on the worsening ozone pollution over the Beijing-Tianjin-Hebei region via influencing photolysis rates

Due to the implementation of the toughest-ever emission control actions across China from 2013 to present, the aerosols are decreasing annually but ozone is simultaneously increasing, especially over the Beijing-Tianjin-Hebei (BTH) region, where ozone pollution can even spread into winter. Quantifying each impact of aerosols on ozone in all seasons is urgent for the worsening ozone pollution in the improved aerosol air quality. In this study, we focused on the impact of aerosols on ozone via influencing photolysis rates. The air pollutants were simulated over the Central East China (CEC) in 2018 by using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. By implementing emissions with base years of 2014 and 2018, we quantified the increase in ozone (ΔOzone_photolysis) caused by the decreasing aerosol concentrations (ΔPM_2.5) by influencing photolysis rates over the BTH region in all seasons. Furthermore, combined with the ozone observations, the contribution of ΔOzone_photolysis to the total changes in ozone (ΔOzone_total) in all seasons was quantitatively discussed. Our results showed that ΔPM_2.5 showed obvious seasonal variations, which PM_2.5 decreased more significantly in winter and autumn than in spring and summer, although significant reductions in anthropogenic emissions were observed in all seasons. Consistent seasonal variations were also observed in ΔOzone_photolysis, and the mean increases reached 5.5 μg m^-3, 2.6 μg m^-3, 1.2 μg m^-3, and 1.4 μg m^-3 in winter, autumn, spring, spring, and summer, respectively. Compared with ΔOzone_total, ΔOzone_photolysis accounted for 36.3%, 17.2%, 3.5% and 10.6% of ΔOzone_total in winter, autumn, spring, and summer, respectively, suggesting that ΔOzone_photolysis was not the primary contributor to the current changes in ozone over the BTH region. However, the 36.3% contribution to ΔOzone_total in winter suggested that ΔOzone_photolysis is still an important contributor and should not be ignored when discussing the formation of high ozone episodes occurring in winter.

Associations between Source-Specific Fine Particulate Matter and Mortality and Hospital Admissions in Beijing, China

The health effects of PM_2.5 exposure have become a major public concern in developing countries. Identifying major PM_2.5 sources and quantifying the health effects at the population level are essential for controlling PM_2.5 pollution and formulating targeted emissions reduction policies. In the current study, we have obtained PM_2.5 mass data and used positive matrix factorization to identify the major sources of PM_2.5. We evaluated the relationship between short-term exposure to PM_2.5 sources and mortality or hospital admissions in Beijing, China, using 441 742 deaths and 9 420 305 hospital admissions from 2013 to 2018. We found positive associations for coal combustion and road dust sources with mortality. Increased hospital admission risks were significantly associated with sources of vehicle exhaust, coal combustion, secondary sulfates, and secondary nitrates. Compared to the cool season, excess mortality risk estimates of coal combustion source were significantly higher in the warm season. Our findings show that reducing more toxic sources of PM_2.5, especially coal emissions, and developing clean energy alternatives can have critical implications for improving air quality and protecting public health.

Does the joint prevention and control regulation improve the air quality? A quasi-experiment in the Beijing economic belt during the COVID-19 pandemic

This study aims to clarify the correlation between air pollution of cities in Beijing Economic Belt from a time-varying perspective and estimate effects of joint prevention and control regulation of air pollution. The COVID-19 pandemic provides a unique opportunity. Based on daily data of air quality, we used TVP-VAR model and utilize the pandemic as a quasi-experiment to assess the policies. The results show air pollution in surrounding cities does influence Beijing's air quality, but the relationship has been weakening year by year, mainly due to industrial adjustment which have achieved progress on alleviating the path of air pollution. Therefore, it is necessary to implement joint regulation in areas with serious pollution. Specifically, the relationship between the air quality of Beijing and Zhangjiakou, Chengde, Tianjin decreased as the pandemic became worse. In contrast, there was no significant decline in Langfang and Baoding. So unlike Baoding and Langfang, industrial production increased relationships between air quality of Beijing and the other three cities, which highlights the validity of restrictions. However, restrictions implemented on Baoding and Langfang affect economic development but have little effect on Beijing's air governance. Therefore, joint regulation contributes to realizing sustainable cities, but more targeted policies should be formulated.

Energy, Data, and Decision-Making: a Scoping Review-the 3D Commission

Access to energy is an important social determinant of health, and expanding the availability of affordable, clean energy is one of the Sustainable Development Goals. It has been argued that climate mitigation policies can, if well-designed in response to contextual factors, also achieve environmental, economic, and social progress, but otherwise pose risks to economic inequity generally and health inequity specifically. Decisions around such policies are hampered by data gaps, particularly in low- and middle-income countries (LMICs) and among vulnerable populations in high-income countries (HICs). The rise of "big data" offers the potential to address some of these gaps. This scoping review sought to explore the literature linking energy, big data, health, and decision-making.Literature searches in PubMed, Embase, and Web of Science were conducted. English language articles up to April 1, 2020, were included. Pre-agreed study characteristics including geographic location, data collected, and study design were extracted and presented descriptively, and a qualitative thematic analysis was performed on the articles using NVivo.Thirty-nine articles fulfilled eligibility criteria. These included a combination of review articles and research articles using primary or secondary data sources. The articles described health and economic effects of a wide range of energy types and uses, and attempted to model effects of a range of technological and policy innovations, in a variety of geographic contexts. Key themes identified in our analysis included the link between energy consumption and economic development, the role of inequality in understanding and predicting harms and benefits associated with energy production and use, the lack of available data on LMICs in general, and on the local contexts within them in particular. Examples of using "big data," and areas in which the articles themselves described challenges with data limitations, were identified.The findings of this scoping review demonstrate the challenges decision-makers face in achieving energy efficiency gains and reducing emissions, while avoiding the exacerbation of existing inequities. Understanding how to maximize gains in energy efficiency and uptake of new technologies requires a deeper understanding of how work and life is shaped by socioeconomic inequalities between and within countries. This is particularly the case for LMICs and in local contexts where few data are currently available, and for whom existing evidence may not be directly applicable. Big data approaches may offer some value in tracking the uptake of new approaches, provide greater data granularity, and help compensate for evidence gaps in low resource settings.

Spatio-temporal analysis of urban air pollutants throughout China during 2014-2019

Air pollution control has become the top priority of China's "green development" concept since 2013. The Chinese government has enacted a range of policies and statutes to control contaminant emissions and improve air quality. On the basis of the national air quality ground observation database, the spatial and temporal distribution of air quality index value (AQI), fine particulate matter (PM_2.5), coarse particles (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) were explored in 336 cities throughout China from 2014 to 2019. AQI and most pollutants (except O₃) decreased in concentrations from 2014 to 2019. In 2019, all cities except Henan reached the level 2 of the ambient air quality index, and six cities had a lower ambient air quality index and reached the level 1. Spatially, higher pollutant concentrations were concentrated in large city clusters, whereas the areas with high O₃ concentration were found across the country. Furthermore, central heating was shown to have a negative impact on air quality. The observed AQI value, PM_2.5, PM₁₀, SO₂, NO₂, and CO concentrations were highest in north and northwest China and Henan province in central China. The correlations among pollutants suggest that the main sources of pollutants are fossil fuel combustion, industrial production, and motor vehicle emissions. The influence of meteorological factors on air quality, long-distance transportation, and the transformations of pollutants should be explored in future research.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-021-01043-5.

Spatial and Temporal Characteristics of Environmental Air Quality and Its Relationship with Seasonal Climatic Conditions in Eastern China during 2015-2018

Exploring the relationship between environmental air quality (EAQ) and climatic conditions on a large scale can help better understand the main distribution characteristics and the mechanisms of EAQ in China, which is significant for the implementation of policies of joint prevention and control of regional air pollution. In this study, we used the concentrations of six conventional air pollutants, i.e., carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), fine particulate matter (PM_2.5), coarse particulate matter (PM₁₀), and ozone (O₃), derived from about 1300 monitoring sites in eastern China (EC) from January 2015 to December 2018. Exploiting the grading concentration limit (GB3095-2012) of various pollutants in China, we also calculated the monthly average air quality index (AQI) in EC. The results show that, generally, the EAQ has improved in all seasons in EC from 2015 to 2018. In particular, the concentrations of conventional air pollutants, such as CO, SO₂, and NO₂, have been decreasing year by year. However, the concentrations of particulate matter, such as PM_2.5 and PM₁₀, have changed little, and the O₃ concentration increased from 2015 to 2018. Empirical mode decomposition (EOF) was used to analyze the major patterns of AQI in EC. The first mode (EOF1) was characterized by a uniform structure in AQI over EC. These phenomena are due to the precipitation variability associated with the East Asian summer monsoon (EASM), referred to as the "summer-winter" pattern. The second EOF mode (EOF2) showed that the AQI over EC is a north-south dipole pattern, which is bound by the Qinling Mountains and Huaihe River (about 35° N). The EOF2 is mainly caused by seasonal variations of the mixed concentration of PM_2.5 and O₃. Associated with EOF2, the Mongolia-Siberian High influences the AQI variation over northern EC by dominating the low-level winds (10 m and 850 hPa) in autumn and winter, and precipitation affects the AQI variation over southern EC in spring and summer.

Regional Policies Targeting Residential Solid Fuel and Agricultural Emissions Can Improve Air Quality and Public Health in the Greater Bay Area and Across China

Air pollution exposure is a leading public health problem in China. The majority of the total air pollution disease burden is from fine particulate matter (PM2.5) exposure, with smaller contributions from ozone (O3) exposure. Recent emission reductions have reduced PM2.5 exposure. However, levels of exposure and the associated risk remain high, some pollutant emissions have increased, and some sectors lack effective emission control measures. We quantified the potential impacts of relevant policy scenarios on ambient air quality and public health across China. We show that PM2.5 exposure inside the Greater Bay Area (GBA) is strongly controlled by emissions outside the GBA. We find that reductions in residential solid fuel use and agricultural fertilizer emissions result in the greatest reductions in PM2.5 exposure and the largest health benefits. A 50% transition from residential solid fuel use to liquefied petroleum gas outside the GBA reduced PM2.5 exposure by 15% in China and 3% within the GBA, and avoided 191,400 premature deaths each year across China. Reducing agricultural fertilizer emissions of ammonia by 30% outside the GBA reduced PM2.5 exposure by 4% in China and 3% in the GBA, avoiding 56,500 annual premature deaths across China. Our simulations suggest that reducing residential solid fuel or industrial emissions will reduce both PM2.5 and O3 exposure, whereas other policies may increase O3 exposure. Improving particulate air quality inside the GBA will require consideration of residential solid fuel and agricultural sectors, which currently lack targeted policies, and regional cooperation both inside and outside the GBA.

Characteristics of airborne bacterial communities in indoor and outdoor environments during continuous haze events in Beijing: Implications for health care

There is solid evidence that haze pollution threatens human health owing to the abiotic pollutants it contains. However, the characteristics of airborne bacterial communities in indoor and outdoor environments exhibiting haze occurrence are still unknown. Thus, we examined variations in both indoor and outdoor airborne bacterial communities in Beijing from December 9-27, 2016, a period which included three haze events. The outdoor airborne bacterial communities were clustered into two main groups (Groups I and II), and they shifted between two typical bacterial communities regardless of the haze event. The Chao1, Shannon, and phylogenetic diversity indexes and abundance of dominant classes changed significantly, as did airborne bacterial community type. The indoor airborne bacterial community closely tracked the outdoor bacterial community type, forming two obvious groups supported by Adonis analysis, changes in dominant classes, and bacterial diversity compared to the outdoor group. Furthermore, we found that the airborne bacterial community type could affect the morbidity of respiratory diseases. Daily pneumonia cases were significantly higher in Group I (p = 0.035), whereas daily amygdalitis cases were significantly higher in Group II (p = 0.025). Interestingly, the enriched classes in the indoor environment were quite different from those in the typical airborne bacterial community environment, except for Clostridia, which had significantly higher abundance in both indoor environments. In conclusion, we found that the two indoor and outdoor airborne bacterial community types changed independently of haze events, and the special airborne bacterial community type was closely related to the incidence of pneumonia in the heavy haze season.

Dynamic Changes in Long-Term Exposure to Ambient Particulate Matter and Incidence of Hypertension in Adults

Many countries dedicated in mitigation of air pollution in the past several decades. However, little is known about how air quality improvement affects health. Therefore, we conducted current study to investigate dynamic changes in long-term exposure to ambient particulate matter (PM_2.5) and incidence of hypertension in a large longitudinal cohort. We recruited 134 978 adults aged 18 years or above between 2001 and 2014. All the participants received a series of standard medical examinations, including measurements of blood pressure. The PM_2.5 concentration was estimated using a satellite-based spatiotemporal model at a high resolution (1×1 km²). The change in long-term exposure to PM_2.5 (ΔPM_2.5) was defined as the difference between the values measured during follow-up and during the immediately preceding visit, and a negative value indicated an improvement in PM_2.5 air quality. Time-varying Cox model was used to examine the associations between ΔPM_2.5 and the development of hypertension. The results show that PM_2.5 concentrations increased in 2002, 2003, and 2004, but began to decrease in 2005. Every 5 µg/m³ change in exposure to PM_2.5 (ie, a ΔPM_2.5 of 5 µg/m³) was associated with a 16% change in the incidence of hypertension (hazard ratio, 0.84; 95% CI, 0.82-0.86). Both stratified and sensitivity analyses generally yielded similar results. We found that an improvement in PM_2.5 exposure is associated with a decreased incidence of hypertension. Our findings demonstrate that air pollution mitigation is an effective strategy to reduce the risk of cardiovascular disease.

Sensitivity of projected PM2.5- and O3-related health impacts to model inputs: A case study in mainland China

In China, fine particulate matter (PM_2.5) and ground-level ozone (O₃) are anticipated to continuously affect large populations in the coming decades. Simulations of the levels of these pollutants largely depend on emissions inputs, which are highly uncertain both in magnitude and spatial distribution. Our goal was to explore sensitivities of projected changes in PM_2.5- and O₃-related short-term health impacts in mainland China to emissions and other model inputs. We simulated winter PM_2.5 and summer O₃ concentrations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for both 2008 and 2050. We used three emission inventories in 2008 and four emissions scenarios in 2050. The resulting air pollutant concentrations were combined with eight population projections and three concentration-response functions (CRFs) to estimate future PM_2.5- and O₃-related health impacts including total, cardiovascular, and respiratory mortalities in mainland China. Multivariate analysis of variance was used to apportion the uncertainty due to different model parameters. Combinations of different parameters produced a wide range of national PM_2.5- and O₃-related mortalities. CRFs and present emissions each contribute 38%-56% and 20%-28% of the total sum of squares for PM_2.5-related mortalities. Future emissions are the largest source of uncertainty in O₃-related mortality estimates, contributing 24%-48% of total sum of squares. Our results suggest that conducting more epidemiological studies and constraining the present day emissions are essential for projecting future air pollutant-related health impacts in mainland China.