Beijing, as a representative megacity in China, is experiencing some of the most severe air pollution episodes in the world, and its fast urbanization has led to substantial urban and peri-urban disparities in both health status and air quality. Uncertainties remain regarding the possible causal links between individual air pollutants and health outcomes, with spatial comparative investigations of these links lacking, particularly in developing megacities. In light of this challenge, Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing (AIRLESS) was initiated, with the aim of addressing the complex issue of relating multi-pollutant exposure to cardiopulmonary outcomes. This paper presents the novel methodological framework employed in the project, namely (1) the deployment of two panel studies from established cohorts in urban and peri-urban Beijing, with different exposure settings regarding pollution levels and diverse sources; (2) the collection of detailed measurements and biomarkers of participants from a nested case (hypertensive) and control (healthy) study setting; (3) the assessment of indoor and personal exposure to multiple gaseous pollutants and particulate matter at unprecedented spatial and temporal resolution with validated novel sensor technologies; (4) the assessment of ambient air pollution levels in a large-scale field campaign, particularly the chemical composition of particulate matter. Preliminary results showed that there is a large difference between ambient and personal air pollution levels, and the differences varied between seasons and locations. These large differences were reflected on the different health responses between the two panels.
Air pollution has been widely recognized as a major risk factor for human
health, especially for cardiopulmonary morbidity and mortality. According to
the Global Burden of Disease (GBD) study, exposure to ambient particulate
matter of aerodynamic diameter
The rapid urbanization process, especially in some Chinese megacities, such as Beijing, has resulted in substantial urban and peri-urban disparities. This is reflected not only in health status due to differences in social economics and health services (Li et al., 2016), but also in the spatial contrast in air pollution in the greater Beijing area (Zhao et al., 2009; Wu et al., 2018; Xu et al., 2011). These contrasts in air pollution are partly driven by the variation in energy use (e.g. in winter, urban areas are dominated by centralized gas heating systems, while traditional biomass and coal stoves remain the key emission source for heating and cooking in peri-urban areas) and provide a unique opportunity to investigate their health impacts on local residents. Such comparative targeted investigations are however largely lacking to date, especially in rapidly developing countries such as China.
In light of these concerns, a consortium of UK and Chinese researchers developed Air Pollution and Human Health in a Chinese megacity Research Programme (APHH) (Shi et al., 2019). APHH includes four complementary research themes: (I) sources and emissions of urban atmospheric pollution, (II) processes affecting urban atmospheric pollution, (III) air pollution and health, and (IV) interventions and solutions. AIRLESS (“Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing”) is nested within theme III of the APHH programme.
Based on two established cohorts in the urban and peri-urban area of greater
Beijing, AIRLESS recruited two panels of non-smoking participants and
completed four repeated follow-up clinical measurements during winter (2016)
and summer (2017). Within the APHH programme, AIRLESS brings together a
detailed integrated database of ambient, personal, and indoor measurements of
a wide range of air pollutants and biomarkers, providing an unprecedented
opportunity to test a variety of hypotheses on the adverse cardiopulmonary
and metabolic effects of air pollution. Using these, the AIRLESS project is
addressing several important research gaps that have been challenging to
tackle due to methodological limitations of conventional air pollution
epidemiology, as listed below.
Investigate the susceptibility of hypertensive individuals to the
adverse effect of air pollution. Hypertension and air pollution are the third and fourth leading
risk factors of mortality in China (WHO, 2017). More
specifically, age-specific hypertension prevalence in China is reported as being
13.0 %, 36.7 %, and 56.5 % among persons aged 20–44, 45–64, and Establish reliable links between air pollution and health effects
by reducing exposure misclassification. Measurement of ambient air pollutants based on ground site observations and
outputs from satellite and chemical transport models are often used as a
proxy for personal exposure in epidemiological studies. However, accumulating
evidence indicates that personal levels are poorly correlated with ambient
levels due to variations of local sources, microenvironmental settings,
and individual behavioural activities (Chatzidiakou et al., 2020). The
difference between true exposure levels and ambient measurement is referred
to as exposure misclassification, which might be larger in peri-urban areas
where infrastructures are sparse and high levels of household air pollution
(HAP) are common, thus biassing the estimation of exposure–response
relationships (Steinle et al., 2013). AIRLESS takes advantage
of the rapid advancement in low-cost sensors to provide highly resolved,
validated personal exposure metrics (Chatzidiakou et al., 2019). Differentiate source-related health effects of air pollution. Humans are exposed to a complex mixture of gaseous and particulate
pollutants emitted from a range of sources and/or arising from different
chemical reactions. Although epidemiological studies worldwide have reported
associations between reduced cardiorespiratory health and increased
concentrations of air pollutants, such as particulate matter (PM), ozone
(O Investigate the underlying mechanisms relating air pollution and health. Although existing epidemiological evidence strongly supports a causal
relationship between PM
The overall aim of the AIRLESS project is to investigate the associations
between exposure to multiple air pollutants and changes in health outcomes,
with a focus on cardiopulmonary biomarkers in urban and peri-urban residents
in Beijing. The specific objectives are as follows:
to recruit two panels, each comprising of 120 participants from existing
cohorts in urban and peri-urban Beijing; to establish infrastructure at urban and peri-urban sites to measure
meteorological parameters and gaseous and particulate pollutant concentrations,
including detailed chemical composition and size-fractional particles using
state-of-the-art instrumentation; to use novel personal air quality monitors (PAMs) and portable instruments to
assess personal and residential exposure to key ambient and household air
pollutants; to develop a time–activity–location model using parameters collected with
the PAMs as inputs to investigate the frequency, duration, and magnitude of
each participant's personal exposure to identify high-risk daily-life
activities to assess cardiopulmonary health in the two panels; to quantitatively compare the air-pollution-associated health responses
between urban and peri-urban areas and between hypertensive and healthy
participants, across winter and summer seasons.
Design scheme of AIRLESS.
The AIRLESS study design is shown in Fig. 1. The project is designed as a panel study with repeated clinical measurements in both winter and summer seasons. Two panels of urban and peri-urban participants were recruited from two existing cohorts. Intensive ambient air pollution monitoring campaigns were launched simultaneously close to the participants' residences at two urban locations and one peri-urban location in Beijing during winter (7 November–21 December 2016) and summer field campaigns (22 May–21 June 2017). Detailed descriptions of the ambient air pollution monitoring campaign and clinic examinations are described in Sect. 2.3 to 2.7.
The two existing cohorts approached to construct the AIRLESS panels are the Chinese Multi-provincial Cohort Study (CMCS) (Liu et al., 2004) from urban Beijing and the International Population Study on Macronutrients and BP (INTERMAP; Yan et al., 2020) in peri-urban Beijing.
CMCS was initiated in 1992 with the inclusion of 30 121 Chinese adults aged 35 to 64 years from 11 provinces in China. It was established with the aim to explore risk factors that contribute to chronic diseases' occurrence and progress, mainly focusing on cardiovascular and pulmonary diseases. One of the CMCS population samples resides within the communities scattered around Peking University (PKU) Hospital, which is located north-west of the 4th Ring Road of Beijing.
The INTERMAP study is an epidemiological investigation aiming to clarify the role of multiple dietary factors in the aetiology of high blood pressure levels prevailing among mostly middle-aged and older individuals. The cohort comprised 4680 men and women aged 40–59 years from 17 diverse population samples in China, Japan, UK, and United States, with one sample from Pinggu District, a peri-urban area to the eastern end of Beijing with agriculture as the main sector of the local economy. All Pinggu participants reside in a number of local villages. INTERMAP is one of a few cohorts in peri-urban China with historical records on the pattern of energy use (Carter et al., 2020). The breadth and depth of high-quality data in both cohorts provide an excellent complement to the new data and bio-samples collected in AIRLESS.
Locations of the two cohorts and three monitoring sites in urban and peri-urban Beijing. The figure is based on Google Maps © Google Maps.
To re-enrol 120 participants from CMCS and 120 participants from INTERMAP, new infrastructure was established for the clinical examination of participants at the Peking University Hospital (urban site) and at Xibaidian Village, Pinggu (peri-urban site), which are about 70 km apart (Fig. 2).
Recruitment criteria.
Screening steps for recruitment in AIRLESS. N refers to the sample size of CMCS cohort, and M refers to that of INTERMAP cohort; the number after letters N and M refers to the screening layer.
Based on the latest follow-up records for both cohorts, the available sample size of participants during AIRLESS recruitment period was 1252 (CMCS) and 177 (INTERMAP). Screening criteria for participant recruitment included personal factors, such as age, smoking status, health condition, and residential address (Table 1). After screening, the number of potential participants who met recruitment criteria were 1252 and 88 at the urban and peri-urban sites, respectively. Because the number of eligible participants of INTERMAP was insufficient due to the high prevalence of smoking in the existing cohort, we recruited a further 90 non-smoking participants from the surrounding villages that fit the same criteria. In total, the sample size of eligible participants from the peri-urban site was then 178. Potential participants were randomly contacted through telephone calls or face-to-face meetings to discuss the project and to make appointments for clinical examinations during the two intensive campaign periods. Final recruitment figures were 123 and 128 participants at the urban and peri-urban clinics respectively. A subgroup of 39 urban and 33 peri-urban participants were further selected for a pilot residential exposure monitoring deployment as described in Sect. 2.4. The detailed screening steps are shown in Fig. 3.
Upon recruitment, written informed consent was obtained from all participants prior to study commencement. The study protocol was approved by the Institutional Review Board of the Peking University Health Science Centre, China (IRB00001052-16028), and the College Research Ethics Committee of King's College London, UK (HR-16/17-3901).
The following information was collected through a baseline questionnaire
after enrolment:
demographic information (e.g. gender, age, education, income) current and past domestic energy use patterns (e.g. types of fuels and
stoves, frequency of cooking and heating stove use) building characteristics active and second-hand smoking history dietary habits (e.g. consumption of alcohol, coffee/tea, sugar beverage
drinking, fried food, vegetables) sleep quality daily activity patterns (transportation, exercise, and potential exposure
sources) major health conditions, events, and diagnoses of non-cardiovascular
outcomes since the original enrolment regular medication or supplement usage.
The matrix of exposure parameters in the AIRLESS study.
A comprehensive dataset of ambient pollution metrics was collected in both
seasons as part of Theme I, II, and III (AIRLESS) of the APHH research programme
(Shi et al., 2019). Urban measurements were performed at two existing air
quality monitoring stations with historical air pollution data, and
peri-urban measurements were obtained from a newly established monitoring
site adjacent to the clinic in Pinggu District. The urban and peri-urban
clinics were both less than 500 m away from the nearby monitoring
station, and most participants' residential addresses were in close
proximity to the sites. The details of the three fixed stations are as
follows:
The same core instruments were deployed to all three sites (Table 2) with
slight differences for certain pollutants between the sites. The collected
measurements resulted in a comprehensive dataset of meteorological
parameters, gaseous pollutants (CO, NO
Residential exposure of subgroups from AIRLESS panels were measured during
both winter and summer campaigns. At the urban site, measurements were
conducted in the homes of participants (
Two commercial portable real-time monitors, namely, a MA300/350
multi-wavelength aethalometer (Aethlabs, USA) and MicroPEM v3.2 (RTI
International, USA) were deployed for residential exposure measurements of
black carbon and PM
A key methodological strength of the AIRLESS project is the assessment of
personal exposure to air pollution at a high spatial and temporal
resolution. Taking advantage of recent advancements in sensor technology and
computational techniques, a novel highly portable monitor (
The characterization of the performance of the air quality sensors
integrated in the PAM is presented in a previous publication
(Chatzidiakou et al., 2019). Briefly, all PAMs were calibrated in two
outdoor co-location deployments at the urban PKU site next to reference
instrumentation for 1 month after the winter and summer deployments to
participants. The performance of the NO
In total 60 devices were deployed at the urban and peri-urban clinic sites, which enabled the recruitment of 30 participants from each site each week (Fig. S1). The PAM was deployed in an easy-to-use carry case for protection, and each participant was instructed to carry the PAM for 1 week of their normal daily life. No other interference was required by the participants than to place it in the base station each night for charging and data transmission. Participants were informed that the monitors utilize GPS technology and were reassured that this information would not be accessed in real time but only used at the end of the study to analyse overall spatial and temporal relationships of fully anonymized data.
The collection of auxiliary parameters, such as timestamped geo-coordinated measurements, background noise, and accelerometer readings, enables the classification of time–activity–location events with an automated algorithm. The algorithm is a progressive composite model that employs spatio-temporal clustering, rule-based models, and machine learning techniques. This enables the investigation of duration, frequency, and magnitude of personal exposure in different microenvironments in daily life and the estimation of activity-weighted exposure at the individual level, often used as a proxy for “dose” (Chatzidiakou et al., 2020). The classifications include core location categories (“home”, “work”, “other indoor static”, “other outdoor static”, “travel”), as well as activities (“cooking”, “sleeping”) and modes of transport (“walk”, “cycle”, “motorbike”, “car/bus”, “train/tube”).
Measurement plans for health outcomes in AIRLESS study.
AP: augmentation pressure. AIx: augmentation index. EBC:
exhaled breath condensate. PEF: peak expiratory flow.
FE
Scheme of clinical examination of AIRLESS.
Each participant was asked to complete a 7 d follow-up session in the winter and summer when the intensive air pollution measurement campaigns launched simultaneously (Fig. 4). Details of the clinical procedures and measurements are described below and listed in Table 3.
Each participant was provided with a PAM and instructed to carry it with
them during their daily activities and to keep it in the bedroom during
night-time to obtain 1-week personal exposure measurements. Basic anthropometry measurements, such as weight, height, and hip and waist
circumference, were obtained.
Participants were asked to complete a follow-up questionnaire on their
domestic fuel use, exposure, activities, medication use, and any sleep
disturbance over the past 3 d. Three consecutive measurements of brachial artery blood pressure were taken
using a digital automatic blood pressure gauge for each participant in a
sitting position after resting for 5 min. Three consecutive measurements of vascular function were taken, including central
(aortic) blood pressure, arterial stiffness parameters (augmentation
pressure (AP), augmentation index (AI), ejection duration (ED), and the
subendocardial viability ratio (SEVR)) for each participant in a supine
position using a pulse wave analysis system developed by the Chinese Academy of
Sciences (Zhang et al., 2012). Each participant was provided with a peak flow meter (Williams Medical, UK)
and was instructed to perform three consecutive peak expiratory flow (PEF)
measurements every morning during the participation week together with
self-reported respiratory symptoms in a diary card. 4 L of breath was collected in an aluminium air-sampling bag. Exhaled
NO (FE 1 mL of exhaled breath condensate (EBC) was collected using a Jaeger
EcoScreen collector (Erich Jaeger, Friedberg, Germany) and was used for
analysis of pH values and inflammatory cytokines. Each participant was provided with a 15 mL polypropylene tube and was
instructed to collect the midstream of their first morning urine sample. Before the blood sample, collection participants were asked to fast overnight
(
Urine samples were stored at
Counts of white blood cells (WBCs), neutrophils, monocytes, lymphocytes, red
blood cells, and haemoglobin and platelets were measured immediately in
the local clinic after blood collection. Levels of glucose-related parameters
(fasting glucose, insulin, and homeostatic model assessment of insulin
resistance (HOMA-IR)), lipid-related parameters (triglyceride (TG),
high-density lipoprotein (HDL), low-density lipoprotein (LDL), and total
cholesterol (Chol)), and C-reactive protein (CRP) were measured 1 month
after the end of each campaign in the Anzhen Hospital in central Beijing.
Further biochemical analyses included (1) multiple cytokines in EBC and the
remaining blood samples, including interleukin 1 alpha (IL-1
One of the main analyses in this study is the associations between air
pollutants and the changes in multiple cardiopulmonary biomarkers. Based on
a sample size of 240 participants, we examine the minimum detectable effect
of PM
In the AIRLESS project we aim to (1) examine the associations between multiple air pollutants and a wide range of cardiopulmonary changes; (2) compare the difference of biological changes in urban and peri-urban settings across seasons; (3) determine if these associations differ in potential susceptible participants, e.g. those with hypertension or other underlying cardiopulmonary disease.
A master database was built to link the data obtained from ambient,
residential, and personal exposure to air pollutants, health outcomes, and
baseline and follow-up questionnaires. Mixed linear effect models with
distributed lag structures will be applied to examine the associations
between air pollutants and health outcomes. The model will include a single
random intercept for participant and assumed equi-correlation between all
observations assigned to each participant. Multiple variables will be
controlled in the model, including age, sex, body mass index (BMI), smoking
status, medication usage, history of diseases, and day of week. Temperature
and relative humidity (RH) will also be adjusted with a non-linear function
integrating specific parameters determined by the minimum of Akaike
information criterion (AIC). We will estimate the changes in biomarker
concentration associated with each interquartile range increase in pollutant
concentrations in the 24 h before the clinic visit, as well as the
previous 1–7 d. To examine the effect of air pollutant on multiple
biomarkers (e.g. metabolome and transcriptome), the false discovery rate (FDR)
adjusted
Statistical power as functions of the detectable effect and
covariance structure for cardiopulmonary outcomes. Power curves are
calculated by using a sample size of 240, within-participant correlation
coefficients (as denoted by rho) of 0.5 and 0.8, and estimated SDs of 7.0, 5.2,
6.9, and 1.69 for outcomes SBP, DBP, FE
Seasonal and spatial trend of ambient PM
Statistic summary of demographic characteristics of urban and peri-urban participants.
Participant compliance with the study protocol for personal
exposure measurements during the winter season. Panels
We recruited 251 participants (urban
Time series of the air pollution exposure of participant U123 in the AIRLESS project (heating season). Personal exposure measurements (blue) are compared to data from the closest monitoring station to the participant's home (red). Grey and white areas indicate the participant is outdoors or at home respectively (based on time–activity model).
Figure 6 shows the ambient PM
Box plots of ambient and personal air pollution levels in urban and peri-urban Beijing during the winter and summer campaigns. The white whisker box plots illustrate outdoor air pollution levels measured at the reference monitoring stations during the winter and summer campaigns. The blue box plots show the levels measured with 60 PAMs (dark and light blue for winter and summer respectively) deployed to 251 participants during the same periods. IQR: interquartile range; perc: percentile.
Regarding personal exposure, participants completed 3548 personal days'
measurements (
A representative participant (U123) was selected to illustrate the concept
of exposure misclassification in Fig. 8. Personal exposure measurements of
participant U123 during the winter campaign are compared with data from the
closest monitoring station to the participant's home location (
The personal measurements show that there is a substantial exposure
misclassification that could be introduced when using outdoor measurements
as exposure metrics, particularly during the winter season. Overall, there
were two distinctive profiles consistent between seasons: personal CO and NO
levels were consistently higher than outdoor levels and showed a strong
seasonal variation, with higher levels measured during the winter season.
Conversely, NO
China has undergone rapid transitions with regard to both air quality and public health in the last 3 decades. Driven by fast urbanization, metropolitan cities such as Beijing manifest a unique difference between urban and peri-urban areas regarding both health status (Li et al., 2016) and varying air pollution concentrations with diverse chemical composition (Zhao et al., 2009; Wu et al., 2018; Xu et al., 2011), which may, in part, be responsible for the different health responses of local residents. The burden of public health in China has also seen a marked decline in child mortality and infectious diseases, while cardiovascular disease (CVD) has emerged as the leading cause of death (Yang et al., 2013). Hypertension, as the leading risk factor of CVD in China, was reported with a prevalence of 23.4 % and 23.1 % in urban and rural residents in Chinese adults respectively (Wang et al., 2018b). Many studies investigated the association between short-term PM exposure and CVD-related outcomes in stratified analysis; however, it remains unclear whether hypertension is a significant modification factor (Sacks et al., 2011).
Given the severe air pollution and nationwide hypertension epidemic in China, AIRLESS sets to (a) investigate the interactive effects of air pollution and hypertension, (b) establish more reliable links between air pollution and health effects by reducing exposure misclassification, (c) differentiate source-related health effects of air pollution, and (d) investigate the underlying mechanisms relating air pollution and health. Several novel methodological elements strengthen the design of the AIRLESS study.
Firstly, the study deployed a state-of-the-art and validated PAM to improve the personal exposure assessment to multiple pollutants. The high compliance rate of the participants with the study protocol highlighted the feasibility of collecting personal exposure data at high spatio-temporal resolution matched with detailed health assessments. The preliminary results highlight a clear difference between personal and ambient exposure driven by individual activity patterns, meteorological factors, and the built environment. In line with previous literature, we show the large biases arising from the use of ambient measurements to represent personal exposure in most epidemiological studies and the potential of novel sensor technologies to revolutionize future human-based studies.
Secondly, time–activity–location patterns of individuals are important determinants of personal exposure, but due to the relative difficulty of collecting such information, they have rarely been taken into account in air pollution epidemiology. For the relatively sedentary participants of this panel study, the home environment was the major contributor to overall exposure and an important modifier of personal concentrations for all investigated air pollutant species. Exposure differences between the two panels were attributed partly to the variation in domestic energy use. For instance, in winter the urban building stock in China relies on centralized gas heating systems, while traditional biomass and coal stoves remain the key emission source for heating and cooking in peri-urban areas. However, the exposure variability between participants was larger than the variability between the two groups, stressing the need to go beyond current methodologies to estimate population exposure.
Lastly, panel studies might be the most suitable way to link intensive air monitoring campaigns for a wide range of pollutant species and personal exposure in different micro-environments, together with epidemiological studies of detailed biological changes in humans. Taking advantage of the simultaneously launched air monitoring campaigns, we successfully collected a rich set of data regarding both exposure and health outcomes. This provides a rare opportunity to investigate the effect of different pollutant species and the underlying biological pathways.
Altogether, the forthcoming outcomes of the AIRLESS project will enhance our understanding of the impact of environmental exposure on human health in a megacity and reinforce evidence-based policies at the appropriate scale that in turn may greatly improve the health and quality of life of China's ageing population.
The study protocol was approved by the Institutional Review Board of the Peking University Health Science Centre, China, and College Research Ethics Committee of King's College London, UK. Written informed consent was obtained from all participants prior to study commencement.
The datasets generated and/or analysed during the current study are not publicly available due to the requirements of the project but are available from the corresponding author on reasonable request.
The supplement related to this article is available online at:
Yiqun Han (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, UK), Wu Chen (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China), Lia Chatzidiakou (Centre for Atmospheric Science, Department of Chemistry, University of Cambridge, Cambridge, UK), Li Yan (Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, UK), Hanbin Zhang (Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, UK), Yanwen Wang (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China), Yutong Cai (Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, UK), Anika Krause (Centre for Atmospheric Science, Department of Chemistry, University of Cambridge, Cambridge, UK), Wuxiang Xie (Peking University Clinical Research Institute, Beijing, China), Yunfei Fan (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China), Teng Wang (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China), Xi Chen (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China), Tao Xue (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China), Gaoqiang Xie (Peking University Clinical Research Institute, Beijing, China), Yingruo Li (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China), Pengfei Liang (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China), Aoming Jin (Peking University Clinical Research Institute, Beijing, China), Yidan Zhu (Peking University Clinical Research Institute, Beijing, China), Yan Luo (Peking University Clinical Research Institute, Beijing, China), Xueyu Han (Department of Epidemiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China), Xinghua Qiu (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China), Queenie Chan (Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London; Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, Imperial College London, London, UK), Ben Barratt (Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, UK), Majid Ezzati (Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, Imperial College London, London, UK), Paul Elliott (Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, Imperial College London, London, UK), Rod Jones (Centre for Atmospheric Science, Department of Chemistry, University of Cambridge, Cambridge, UK), Jing Liu (Department of Epidemiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China), Yangfeng Wu (Peking University Clinical Research Institute, Beijing, China; The George Institute for Global Health at Peking University Health Science Center, Beijing, China), Meiping Zhao (College of Chemistry, Peking University, Beijing, China), Junfeng Zhang (Duke Kunshan University, Nanjing, China), Frank J. Kelly (Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, UK), Tong Zhu (BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China).
All authors have disclosed that there are no actual or potential competing interests regarding the submitted article.
TZ and FK are co-principal investigators of AIRLESS, and they designed the study and revised the manuscript. YH participated in the study design, coordinated air pollution monitoring and clinical measurements at the Pinggu site, and drafted the manuscript. WC coordinated the clinical measurements at the PKU site. LC, AK, and RJ developed the personal monitor PAM and were involved in the monitor deployment and data ratification. YH, LY, HZ, XC, YC, WX, AJ, YZ, and YL are key staff who participated in the clinical measurements at the Pinggu site. WC, YW, TX, YF XH, and TW are key staff who participated in the clinical measurements at the PKU site. HZ and BB participated in the residential air pollution measurement. XQ, MZ, and JZ were involved in the design of laboratory biomarkers. JL coordinated the CMCS cohort, and YL, XG, and QC coordinated the INTERMAP cohort. ME, PE, RJ, JL, MZ, JZ, and YW are co-investigators of the AIRLESS study and revised the manuscript. All authors read and approved the final version of the paper.
This article is part of the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
We are greatly thankful to all the members of the AIRLESS team who helped to accomplish the fieldwork at the urban and peri-urban Beijing sites. We would also like to thank Roy Harrison and Zongbo Shi for organizing and coordinating the APHH programme. We also appreciate the AIRPOLL and AIRPRO study team for the collected data of ambient pollutants for reference calibration and further health analysis.
This research has been supported by the National Natural Science Foundation of China (grant no. 81571130100) and the Natural Environment Research Council of UK (grant nos. NE/N007018/1 and NE/S006729/1).
This paper was edited by Pingqing Fu and reviewed by two anonymous referees.