Significant chlorine emissions from biomass burning affect the long-term atmospheric chemistry in Asia.

Biomass burning (BB) is a major source of trace gases and particles in the atmosphere, influencing air quality, radiative balance, and climate. Previous studies have mainly focused on the BB emissions of carbon and nitrogen species with less attention on chlorine. Reactive chlorine chemistry has significant effects on atmospheric chemistry and air quality. However, quantitative information on chlorine emissions from BB, particularly the long-term trend and associated atmospheric impacts, is limited both on regional and global scales. Here, we report a long-term (2001-2018) high-resolution BB emission inventory for the major chlorine-containing compounds (HCl, chloride, and CH3Cl) in Asia based on satellite observations. We estimate an average of 730 Gg yr-1 chlorine emitted from BB activity in Asia, with China contributing the largest share at 24.2% (177 Gg yr-1), followed by Myanmar at 18.7% and India at 18.3%. Distinct seasonal patterns and significant spatial and interannual variability are observed, mainly driven by human-mediated changes in agricultural activity. By incorporating the newly developed chlorine emission inventory into a global chemistry-climate model (CAM-Chem), we find that the BB-chlorine emissions lead to elevated levels of HCl and CH3Cl (monthly average up to 2062 and 1421 parts per trillion by volume (pptv), respectively), subsequently resulting in noticeable changes in oxidants (up to 3.1% in O3 and 17% in OH radicals). The results demonstrate that BB is not only a significant source of air pollutants but also of oxidants, suggesting a larger role of BB emissions in the atmospheric chemistry and oxidation process than previously appreciated. In light of the projected increase in BB activity toward the end of the century and the extensive control of anthropogenic emissions worldwide, the contribution of BB emissions may become fundamental to air quality composition in the future.

The joint effect of long-term exposure to multiple air pollutants on non-accidental and cause-specific mortality: A longitudinal cohort study.

The long-term joint impacts of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) on mortality are inconclusive. To bridge this research gap, we included 283,568 adults from the Taiwan MJ cohort between 2005 and 2016 and linked with the mortality data until 31 May 2019. Participants' annual average exposures to PM2.5, NO2, and O3 were estimated using satellite-based spatial-temporal models. We applied elastic net-regularised Cox models to construct a weighted environmental risk score (WERS) for the joint effects of three pollutants on non-accidental, cardiovascular, and cancer mortality and evaluated the contribution of each pollutant. The three pollutants jointly raised non-accidental mortality risk with a WERS hazard ratio (HR) of 1.186 (95% CI: 1.118-1.259) per standard deviation increase in each pollutant and weights of 72.8%, 15.2%, and 12.0% for PM2.5, NO2, and O3, respectively. The WERS increased cardiovascular death risk [HR: 1.248 (1.042-1.496)], with PM2.5 as the first contributor and O3 as the second. The WERS also elevated the cancer death risk [HR: 1.173 (1.083-1.270)], where PM2.5 played the dominant role and NO2 ranked second. Coordinated control of these three pollutants can optimise the health benefits of air quality improvements.

Chronic exposure to ambient air pollution and the risk of non-alcoholic fatty liver disease: A cross-sectional study in Taiwan and Hong Kong.

BACKGROUND: Information on the relation of air pollution with non-alcoholic fatty liver disease (NAFLD) is scarce. We thus conducted a large cross-sectional study in Asia to investigate the role of air pollution in NAFLD. METHODS: We recruited 329,048 adults (mean age: 41.0 years) without other liver disease (hepatitis and cirrhosis) or excessive alcohol consumption in Taiwan and Hong Kong from 2001 to 2018. The concentrations of nitrogen dioxide (NO2) and ozone (O3) were estimated using a space-time regression model, and the concentrations of fine particulate matter (PM2.5) was evaluated using a satellite-based spatio-temporal model. NAFLD was determined using either the fatty liver index (FLI) or the hepatic steatosis index (HSI). The NAFLD-related advanced fibrosis was defined according to BARD score or the fibrosis-4 (FIB-4). A logistic regression model was adopted to explore the relationships of ambient air pollution with the odds of NAFLD and NAFLD-related advanced fibrosis. RESULTS: We found positive relationships between PM2.5 and the odds of NAFLD and advanced fibrosis, with every standard deviation (SD, 7.5 µg/m3) increases in PM2.5 exposure being associated with a 10% (95% confidence interval [CI]: 9%-11%) increment in the prevalence of NAFLD and an 8% (95% CI: 7%-9%) increment in the prevalence of advanced fibrosis. Similarly, the prevalence of NAFLD and advanced fibrosis increased by 8% (95% CI: 7%-9%) and 7% (95% CI: 6%-8%) with per SD (18.9 µg/m3) increasement in NO2 concentration, respectively. Additionally, for every SD (9.9 µg/m3) increasement in O3 concentration, the prevalence of NAFLD and advanced fibrosis decreased by 12% (95% CI: 11%-13%) and 11% (95% CI: 9%-12%), respectively. CONCLUSION: Higher ambient PM2.5 and NO2 are linked with higher odds of NAFLD and advanced fibrosis. Our findings indicate that reducing PM2.5 and NO2 concentrations may be an effective way for preventing NAFLD. Further studies on O3 are warranted.

Fertilizer management for global ammonia emission reduction.

Crop production is a large source of atmospheric ammonia (NH3), which poses risks to air quality, human health and ecosystems1-5. However, estimating global NH3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy4,5. Here we develop a machine learning model for generating crop-specific and spatially explicit NH3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr-1, lower than previous estimates that did not fully consider fertilizer management practices6-9. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr-1) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44-56%) for rice, 27% (24-28%) for maize and 26% (20-28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1-2.6 and 5.5 ± 5.7% under SSP5-8.5 by 2030-2060. However, targeted fertilizer management has the potential to mitigate these increases.

Effects of meteorological conditions on the mixing height of Nitrogen dioxide in China using new-generation geostationary satellite measurements and machine learning.

Nitrogen dioxide (NO2) plays a critical role in terms of air quality, human health, ecosystems, and its impact on climate change. While the crucial roles of the vertical structure of NO2 have been acknowledged for some time, there is currently limited knowledge about this aspect in China. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first geostationary satellite instrument capable of measuring the hourly columnar amount of NO2. The study presented here introduces the use of mixing height for NO2 in the atmosphere. A thorough examination of spatiotemporal variations in the mixing height of NO2 was conducted using data from both the GEMS and ground-based air quality monitoring networks. A random forest model based on machine learning techniques was utilized to examine how meteorological parameters affect the mixing height of NO2. The results of our study reveal a notable seasonal fluctuation in the mixing height of NO2, with the highest values observed during the summer and the lowest values during the winter. Additionally, there was an increasing diurnal trend from early morning to mid-afternoon. Moreover, the study discovered elevated NO2 mixing heights in the dry regions of northern China. The results also indicated a positive correlation between the mixing height of NO2 and temperature and wind speed, while negative associations were found with relative humidity and air pressure. The machine learning model's predicted NO2 mixing heights were in good agreement with the measurement-based outcomes, as evidenced by a coefficient of determination (R2) value of 0.96 (0.84 for the 10-fold cross-validation). These findings emphasize the noteworthy influence of meteorological variables on the vertical distribution of NO2 in the atmosphere and enhance our comprehension of the three-dimensional variations in NO2.

Relative contributions of ambient air and internal sources to multiple air pollutants in public transportation modes.

Commuters are often exposed to relatively high air pollutant concentrations in public transport microenvironments (TMEs) because of their proximity to emission sources. Previous studies have mainly focused on assessing the concentrations of air pollutants in TMEs, but few studies have distinguished between the contributions of ambient air and internal sources to the exposure of commuters to air pollutants. The main objective of this study was to quantify the contributions of ambient air and internal sources to the measured particulate matter and gaseous pollutant concentrations in selected TMEs in Hong Kong, a high-rise, high-density city in Asia. A sampling campaign was conducted to measure air pollutant concentrations in TMEs in Hong Kong in July and November 2018 using portable air quality monitors. We measured the concentrations of each pollutant in different TMEs and quantified the infiltration of particulate matter into these TMEs. The double-decker bus had the lowest particulate matter concentrations (mean PM1, PM2.5, and PM10 concentrations of 5.1, 9.5, and 13 µg/m3, respectively), but higher concentrations of CO (0.9 ppm), NO (422 ppb), and NO2 (100 ppb). For all the TMEs, about half of the PM2.5 were PM1 particles. The Mass Transit Railway (MTR) subway system had a PM2.5/PM10 ratio of about 0.90, whereas the PM2.5/PM10 ratio was about 0.60-0.70 for the other TMEs. The MTR had infiltration factor estimates <0.4 for particulate matter, lower than those of the double-decker bus and minibus. The MTR had the highest contribution from internal sources (mean PM1, PM2.5, and PM10 concentrations of 4.6, 13.4, and 15.8 µg/m3, respectively). This study will help citizens to plan commuting routes to reduce their exposure to air pollution and help policy-makers to prioritize effective exposure reduction strategies.

Association of ambient PM2.5 concentration with tuberculosis reactivation diseases-an integrated spatio-temporal analysis.

Objectives: While the plausible role of ambient particulate matter (PM)2.5 exposure in tuberculosis (TB) reactivation has been inferred from in vitro experiments, epidemiologic evidence is lacking. We examined the relationship between ambient PM2.5 concentration and pulmonary TB (PTB) in an intermediate TB endemicity city dominated by reactivation diseases. Methods: Spatio-temporal analyses were performed on TB notification data and satellite-based annual mean PM2.5 concentration in Hong Kong. A total of 52,623 PTB cases from 2005-2018 were mapped to over 400 subdistrict units. PTB standardized notification ratio by population subgroups (elderly aged ≥65, middle-aged 50-64, and young adults aged 15-49) was calculated and correlated with ambient PM2.5 concentration. Results: Significant associations were detected between high ambient PM2.5 concentration and increased PTB among the elderly. Such associations were stable to the adjustment for socio-economic factors and other criteria pollutants. Unstable patterns of association between PM2.5 and PTB risk were observed in the middle-aged population and young adults, for which the observed associations were confounded by other criteria pollutants. Conclusion: With elderly PTB almost exclusively attributable to reactivation, our findings suggested that increased TB reactivations have occurred in association with high ambient PM2.5 exposure, lending support to preventive measures that minimize PM2.5-related TB reactivation.

Concurrent measurements of nitrate at urban and suburban sites identify local nitrate formation as a driver for urban episodic PM2.5 pollution.

Nitrate (NO3-) is often among the leading components of urban particulate matter (PM) during PM pollution episodes. However, the factors controlling its prevalence remain inadequately understood. In this work, we analyzed concurrent hourly monitoring data of NO3- in PM2.5 at a pair of urban and suburban locations (28 km apart) in Hong Kong for a period of two months. The concentration gradient in PM2.5 NO3- was 3.0 ± 2.9 (urban) vs. 1.3 ± 0.9 µg m-3 (suburban) while that for its precursors nitrogen oxides (NOx) was 38.1 vs 4.1 ppb. NO3- accounted for 45 % of the difference in PM2.5 between the sites. Both sites were characterized to have more available NH3 than HNO3. Urban nitrate episodes, defined as periods of urban-suburban NO3- difference exceeding 2 µg m-3, constituted 21 % of the total measurement hours, with an hourly NO3- average gradient of 4.2 and a peak value of 23.6 µg m-3. Our comparative analysis, together with 3-D air quality model simulations, indicates that the high NOx levels largely explain the excessive NO3- concentrations in our urban site, with the gas phase HNO3 formation reaction contributing significantly during the daytime and the N2O5 hydrolysis pathway playing a prominent role during nighttime. This study presents a first quantitative analysis that unambiguously shows local formation of NO3- in urban environments as a driver for urban episodic PM2.5 pollution, suggesting effective benefits of lowering urban NOx.

Chronic exposure to ambient PM2.5/NO2 and respiratory health in school children: A prospective cohort study in Hong Kong.

Despite increasing concerns about the detrimental effects of air pollution on respiratory health, limited evidence is available on these effects in the Hong Kong population, especially in children. In this prospective cohort study between 2012 and 2017, we aimed to investigate the associations between exposure to air pollution (concentrations of fine particulate matter [PM2.5] and nitrogen dioxide [NO2]) and respiratory health (lung function parameters and respiratory diseases and symptoms) in schoolchildren. We recruited 5612 schoolchildren aged 6-16 years in Hong Kong. We estimated the annual average concentrations of ambient PM2.5 and NO2 at each participant's address using spatiotemporal models. We conducted spirometry tests on all participants to measure their lung function parameters and used a self-administered questionnaire to collect information on their respiratory diseases and symptoms and a wide range of covariates. Linear mixed models were used to investigate the associations between exposure to air pollution and lung function. Mixed-effects logistic regression models with random effects were used to investigate the associations of exposure to air pollution with respiratory diseases and symptoms. In all of the participants, every 5-µg/m3 increase in the ambient PM2.5 concentration was associated with changes of - 13.90 ml (95 % confidence interval [CI]: -23.65 ml, -4.10 ml), - 4.20 ml (-15.60 ml, 7.15 ml), 27.20 ml/s (-3.95 ml/s, 58.35 ml/s), and - 19.80 ml/s (-38.35 ml/s, -1.25 ml/s) in forced expiratory volume in 1 s, forced vital capacity, peak expiratory flow, and maximal mid-expiratory flow, respectively. The corresponding lung function estimates for every 5-µg/m3 increase in the ambient NO2 concentration were - 2.70 ml (-6.05 ml, 0.60 ml), - 1.40 ml (-5.40 ml, 2.60 ml), - 6.60 ml/s (-19.75 ml/s, 6.55 ml/s), and - 3.05 ml/s (-11.10 ml/s, 5.00 ml/s), respectively. We did not observe significant associations between PM2.5/NO2 exposure and most respiratory diseases and symptoms. Stratified analyses by sex and age showed that the associations between exposure to air pollution and lung function parameters were stronger in male participants and older participants (11-14 year old group) than in female participants and younger participants (6-10 year old group), respectively. Our results suggest that chronic exposure to air pollution is detrimental to the respiratory health of schoolchildren, especially that of older boys. Our findings reinforce the importance of air pollution mitigation to protect schoolchildren's respiratory health.

Impacts of pollution heterogeneity on population exposure in dense urban areas using ultra-fine resolution air quality data.

Traditional air quality data have a spatial resolution of 1 km or above, making it challenging to resolve detailed air pollution exposure in complex urban areas. Combining urban morphology, dynamic traffic emission, regional and local meteorology, physicochemical transformations in air quality models using big data fusion technology, an ultra-fine resolution modeling system was developed to provide air quality data down to street level. Based on one-year ultra-fine resolution data, this study investigated the effects of pollution heterogeneity on the individual and population exposure to particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3) in Hong Kong, one of the most densely populated and urbanized cities. Sharp fine-scale variabilities in air pollution were revealed within individual city blocks. Using traditional 1 km average to represent individual exposure resulted in a positively skewed deviation of up to 200% for high-end exposure individuals. Citizens were disproportionally affected by air pollution, with annual pollutant concentrations varied by factors of 2 to 5 among 452 District Council Constituency Areas (DCCAs) in Hong Kong, indicating great environmental inequities among the population. Unfavorable city planning resulted in a positive spatial coincidence between pollution and population, which increased public exposure to air pollutants by as large as 46% among districts in Hong Kong. Our results highlight the importance of ultra-fine pollutant data in quantifying the heterogeneity in pollution exposure in the dense urban area and the critical role of smart urban planning in reducing exposure inequities.

Cardiovascular Mortality, Habitual Exercise, and Particulate Matter 2.5 Exposure: A Longitudinal Cohort Study.

INTRODUCTION: Habitual exercise may amplify the respiratory uptake of air pollutants in the lung, exacerbating the adverse effects of air pollution. However, it is unclear whether this can reduce the health benefits of habitual exercise (referred to as leisure-time exercise). Thus, the combined effects of habitual exercise and chronic exposure to ambient fine particulate matter 2.5 on cardiovascular mortality were examined among adults in Taiwan. METHODS: A total of 384,128 adults were recruited between 2001 and 2016 and followed up to May 31, 2019. Participants' vital status was obtained by matching their unique identification numbers with records of cardiovascular death in the National Death Registry of Taiwan. A time-varying Cox regression model was used to analyze the data. Analyses were conducted in 2021. RESULTS: Cardiovascular death risks were inversely associated with habitual exercise and positively associated with chronic exposure to particulate matter 2.5. The beneficial effects of habitual exercise on cardiovascular mortality were not modified by chronic exposure to particulate matter 2.5. Inactive participants with high particulate matter 2.5 exposure exhibited a 123% higher risk of cardiovascular death than high-exercise-group participants exposed to low levels of particulate matter 2.5 (95% CI=89, 163). CONCLUSIONS: High level of habitual exercise combined with low exposure level of ambient particulate matter 2.5 is associated with the lowest risk of cardiovascular death. A higher level of habitual exercise is associated with a lower risk of cardiovascular death at all levels of particulate matter 2.5 exposure studied. The results indicate that habitual exercise is a safe health promotion strategy even for people residing in relatively polluted regions.

Life-course exposure to ambient fine particulate matter and hypertension in adulthood: a longitudinal cohort study.

PM2.5-hypertension association were well documented in adults, while the effects of life-course exposure to PM2.5 on adulthood hypertension remained unclear. This study aimed to investigate the associations between life-course exposure to ambient PM2.5 and incident hypertension in adulthood in Asia. We included 4272 participants with 17,814 medical visits from two open cohorts in Taiwan and Hong Kong between 2000 and 2018. We used a satellite-based model to assess 2-year average PM2.5 exposure at a resolution of 1 km2. A linear mixed model was used to examine the association with blood pressure. A Cox regression model with time-dependent covariates was used to examine the overall association with the development of hypertension in adulthood. Life-course mixed models were used to examine the effects of PM2.5 exposure at different life stages on blood pressure and hypertension. For every 10 µg/m3 increase in PM2.5, the overall risk of adulthood hypertension increased by 40% (95% confidence interval [CI] 8-80%). The health effects of PM2.5 exposure at different life-stages on incident hypertension were generally independent of each other. In critical model, the risk of developing hypertension increased 23%, 27%, and 55% for each 10 µg/m3 increase in PM2.5 exposure during school age, adolescence, and adulthood, respectively. Similar associations were found between life-course PM2.5 exposure and blood pressure. Association between PM2.5 and adulthood hypertension can be traced back to childhood. Our study suggests that life-course control of air pollution exposure should be implemented to alleviate the huge burden of adulthood hypertension.

Associations between long-term exposure to multiple air pollutants and age at menopause: a longitudinal cohort study.

BACKGROUND: To investigate the associations between long-term exposure to ambient air pollution and age at menopause and the risk of early menopause in two Asian cohorts. METHODS: A total of 53,167 female adults were enrolled from two ongoing cohorts, one each in Taiwan and Hong Kong, between 2003 and 2018, yielding 200,000 person-years of follow-up. We performed a Cox regression model with time-dependent covariates to investigate associations between air pollution and menopause. RESULTS: The mean age at baseline and at natural menopause was 34.4 and 51.0 years, respectively. In the single-pollutant models, we found that increase in PM2.5 and NO2 was associated a younger age at menopause [hazard ratios (HRs) (95% confidence interval, CI): 1.16 (1.09-1.23) and 1.04 (1.01-1.06), respectively, for each 10 µg/m3 increase in air pollution]. In the multiple-pollutant models, the NO2-menopause associations were attenuated after adjusting for PM2.5 and O3, whereas the associations of menopause with PM2.5 and O3 remained stable. Similar associations were observed for the risk of early menopause (age at menopause ≤ 45 years). CONCLUSIONS: Long-term exposure to ambient air pollution was associated with the age at menopause and the risk of early menopause. More effective strategies to mitigate air pollution are required.

Risk tradeoffs between nitrogen dioxide and ozone pollution during the COVID-19 lockdowns in the Greater Bay area of China.

Photochemical regime for ozone (O3) formation is complicated in the sense that reducing emission of nitrogen oxides (NOx) may increase O3 concentration. The lockdown due to COVID-19 pandemic affords a unique opportunity to use real observations to explore the O3 formation regime and the effectiveness of NOx emission control strategies. In this study, observations from ground networks during the lockdowns were used to assess spatial disparity of the Ratio of Ozone Formation (ROF) for nitrogen dioxide (NO2) reduction in the Greater Bay Area (GBA) of China. The health risk model from Air Quality Health Index (AQHI) system in Hong Kong was adopted to evaluate the risk tradeoffs between NO2 and O3. Results show that the levels of O3 increase and NO2 reduction were comparable due to high ROF values in urban areas of central GBA. The ozone reactivity to NO2 reduction gradually declined outwards from central GBA. Despite the O3 increases, the NOx emission controls reduced the Integrated Health Risk (IHR) of NO2 and O3 in most regions of the GBA. When risk coefficients from the AQHI in Canada or the global review were adopted in the risk analyses, the results are extremely encouraging because the controls of NOx emission reduced the IHR of NO2 and O3 almost everywhere in the GBA. Our results underscore the importance of using a risk-based method to assess the effectiveness of emission control measures and the overall health benefit from NOx emission controls in the GBA.

Multi-pollutant air pollution and renal health in Asian children and adolescents: An 18-year longitudinal study.

BACKGROUND: Few studies have examined the effects of multi-pollutant air pollution on renal health, especially in children and adolescents. This study investigated the association between long-term ambient air pollution exposure and renal health in Asian children and adolescents. METHODS: This study included 10,942 children and adolescents from Taiwan and Hong Kong between 2000 and 2017. PM2.5, NO2 and O3 concentrations were estimated using satellite-based spatiotemporal regression models. Two-year average concentrations, those of the year of visit and the preceding year, were used. Linear mixed models were used to examine the association between air pollution and yearly changes in estimated glomerular filtration rate (eGFR). Cox regression models with time-dependent covariates were used to examine the association between air pollution and the development of chronic kidney disease (CKD). RESULTS: Median age of the participants was 19 years (range: 2-25). The overall average concentration of PM2.5, NO2 and O3 was 26.7 µg/m3, 44.1 µg/m3 and 51.1 µg/m3, respectively. The mean yearly change in eGFR was 0.37 µL/min/1.73 m2 and the incidence rate of CKD was 6.8 per 1,000 person-years. In single-pollutant models, each 10 µg/m3 increase in PM2.5 was associated with a 0.45 µL/min/1.73 m2 [95% confidence interval (CI): 0.28-0.63] reduction in the yearly increase in eGFR and 53% [hazard ratio (HR): 1.53 (95%CI: 1.07-2.2)] greater risk of incident CKD. Each 10 µg/m3 increase in NO2 was associated with a 7% [HR (95%CI): 1.07 (1.00-1.15)] higher risk of incident CKD, while an equivalent increase in O3 was associated with a 19% [HR (95%CI): 0.81 (0.67-0.98)] lower risk. CONCLUSIONS: Long-term exposure to ambient PM2.5 and NO2 was associated with a slower growth of eGFR and a higher risk of incident CKD in children and adolescents. Our findings suggest that air pollution control in early life is imperative to improve lifelong renal health and alleviate the CKD burden.

Habitual Exercise, Air Pollution, and Pneumonia Mortality: A Longitudinal Cohort Study of Approximately 0.4 Million Adults.

In this study, we aimed to examine the combined associations of particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5) and habitual exercise with pneumonia mortality. We included 384,130 persons aged ≥18 years from Taiwan, Republic of China, during 2001-2016. We followed participants until May 31, 2019, to obtain information on vital status. A time-dependent Cox regression model was used for statistical analysis. We found that risks of pneumonia mortality were reduced by 55% (hazard ratio (HR) = 0.45, 95% confidence interval (CI): 0.36, 0.55) and 36% (HR = 0.64, 95% CI: 0.52, 0.80) in participants who engaged in high and moderate levels of exercise, respectively, as compared with inactive persons. By contrast, each 10-µg/m3 increase in chronic PM2.5 exposure was associated with a 30% (HR = 1.30, 95% CI: 1.17, 1.45) higher risk of pneumonia mortality. Risk of pneumonia death was 72% lower (HR = 0.28, 95% CI: 0.20, 0.41) for persons with a high exercise level and a low PM2.5 level. Lower risk of pneumonia mortality was associated with both higher exercise and lower PM2.5 air pollution levels. For adults exposed to different levels of PM2.5, exercise benefits remained. Our findings suggest that engaging in exercise is a safe and effective strategy for alleviating the burden of pneumonia mortality, even for people who reside in a moderately polluted area.

Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants.

For the monitoring of urban air pollution, smart sensors are often seen as a welcome addition to fixed-site monitoring (FSM) networks. Due to price and simple installation, increases in spatial representation are thought to be achieved by large numbers of these sensors, however, a number of sensor errors have been identified. Based on a high-resolution modelling system, up to 400 pseudo smart sensors were perturbated with the aim of simulating common sensor errors and added to the existing FSM network in Hong Kong, resulting in 1200 pseudo networks for PM2.5 and 1040 pseudo networks for NO2. For each pseudo network, population-weighted area representativeness (PWAR) was calculated based on similarity frequency. For PM2.5, improvements (up to 16%) to the high baseline representativeness (PWAR = 0.74) were achievable only by the addition of high-quality sensors and favourable environmental conditions. The baseline FSM network represents NO2 less well (PWAR = 0.52), as local emissions in the study domain resulted in high spatial pollution variation. Due to higher levels of pollution (population-weighted average 37.3 ppb) in comparison to sensor error ranges, smart sensors of a wider quality range were able to improve network representativeness (up to 42%). Marginal representativeness increases were found to exponentially decrease with existing sensor number. The quality and maintenance of added sensors had a stronger effect on overall network representativeness than the number of sensors added. Often, a small number of added sensors of a higher quality class led to larger improvements than hundreds of lower-class sensors. Whereas smart sensor performance and maintenance are important prerequisites particularly for developed cities where pollutant concentration is low and there is an existing FSM network, our study shows that for places with high pollutant variability and concentration such as encountered in some developing countries, smart sensors will provide benefits for understanding population exposure.

Habitual exercise, chronic exposure to fine particulate matter and high-sensitivity C reactive protein in Asian adults.

OBJECTIVES: Exercise may increase the inhaled amount of air pollutants and exacerbate the adverse health effects. We investigated the combined effects of chronic exposure to fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) and habitual exercise on C reactive protein (CRP), a sensitive marker of inflammation. METHODS: We selected 40 209 Taiwanese adults who joined a standard medical screening programme between 2001 and 2016. The PM2.5 exposure was estimated at each participant's address using a satellite-based spatiotemporal model. Information on habitual exercise was collected using a standard self-administered questionnaire. Mixed-effects linear regression models were used to investigate the associations of CRP with PM2.5 and exercise. An interaction term of PM2.5 and exercise was introduced in the models to test the modifying effects. RESULTS: A greater amount of habitual exercise was associated with a decreased level of CRP, while a higher concentration of PM2.5 exposure was associated with an increased level of CRP. The inverse associations of habitual exercise with CRP were not modified by chronic exposure to PM2.5. The participants in the group with a low level of exercise and a high level of PM2.5 exposure exhibited a 19.1% higher level of CRP than those in the group with a high level of exercise and a low level of PM2.5 exposure (95% CI: 13.7% to 24.8%; p<0.001). The longitudinal and sensitivity analyses yielded similar results. CONCLUSIONS: Increased levels of exercise and reduced exposure levels of PM2.5 are associated with a lower CRP level. Habitual exercise reduces CRP level regardless of the levels of chronic PM2.5 exposure. Our results support that habitual exercise is a safe approach for reducing systemic inflammation to improve cardiovascular health even for people residing in relatively polluted areas.

Optimized neural network for daily-scale ozone prediction based on transfer learning.

Tropospheric ozone (O3) pollution is worsening in China, and an accurate forecast is a prerequisite to lower the O3 peak level. In recent years, machine learning techniques have attracted increasing attention in O3 prediction owing to their high efficiency and simple operation. However, the accuracy of predicting the daily O3 level is low. This study proposed a novel model by coupling long short-term memory neural network with transfer learning (TL-LSTM), with meteorology and pollutant concentration information as the model input. L2 regularization was applied to reduce the risk of overfitting and to improve the accuracy and generalization ability of the model prediction. Our results indicated that by transferring the knowledge in the model configuration from the hourly LSTM module, TL-LSTM greatly improves the predictability of the daily maximum 8 h average (MDA8) of O3 in Hong Kong. The coefficient of determination (R2) increased from 0.684 to 0.783 and the mean square error (MSE) reduced from 1.36 × 10-2 to 1.05 × 10-2. Furthermore, R2 and MSE were the highest in summer, indicating an under-prediction of peak O3 levels. This was a result of the limited number of high O3 days, which did not provide sufficient knowledge for the model to make an accurate prediction. Sobol analysis indicated that wind speed was the most sensitive factor in O3 prediction, largely due to the development of land-sea breeze circulation which effectively traps pollutants and expedites O3 formation. The results clearly demonstrate the effectiveness of the TL-LSTM in predicting the daily O3 concentration in Hong Kong. Thus, TL-LSTM can be promulgated into other photochemically active regions to assist in O3 pollution forecasting and management.

Chronic fine particulate matter exposure, habitual exercise, and dyslipidemia: A longitudinal cohort study.

BACKGROUND: Physical activity may increase the intake of air pollutants due to a higher ventilation rate, which may exacerbate the adverse health effects. This study investigated the combined effects of habitual exercise and long-term exposure to fine particulate matter (PM2.5) on the incidence of dyslipidemia in a large longitudinal cohort in Taiwan. METHODS: A total of 121,948 adults (≥18 years) who received at least two medical examinations from 2001 to 2016 were recruited, yielding 407,821 medical examination records. A satellite-based spatiotemporal model was used to estimate the 2-year average PM2.5 concentration (i.e., the year of and the year before the medical examination) at each participant's address. Information on habitual exercise within 1 month before the medical examination was collected using a standard self-administered questionnaire. A Cox regression model with time-dependent covariates was used to investigate the combined effects. RESULTS: Compared with inactivity, moderate and high levels of exercise were associated with a lower incidence of dyslipidemia, with hazard ratios (HRs) (95% confidence intervals [CIs]) of 0.91 (0.88, 0.94) and 0.73 (0.71, 0.75), respectively. Participants with a moderate (22.37-25.96 µg/m3) or high (>25.96 µg/m3) level of PM2.5 exposure had a higher incidence of dyslipidemia than those with a low level of PM2.5 exposure (≤22.37 µg/m3), with HRs (95% CIs) of 1.36 (1.32, 1.40), and 1.90 (1.81, 1.99), respectively. We observed a statistically significant, but minor, interaction effect of PM2.5 exposure and exercise on the development of dyslipidemia, with an overall hazard ratios (95% CI) of 1.08 (1.05, 1.10), indicating that an incremental increase in the level of exercise was associated with an 8% increase in the risk of dyslipidemia associated with every 10 µg/m3 increase in PM2.5 exposure. However, the negative association between habitual exercise and dyslipidemia remained, regardless of the level of PM2.5 exposure, suggesting that the benefits of increased habitual exercise outweighed the adverse effects of the increase in PM2.5 intake during exercise. CONCLUSIONS: Increased levels of exercise and reduced levels of PM2.5 exposures were associated with a lower incidence of dyslipidemia. Although an increase in habitual exercise slightly increased the risk of dyslipidemia associated with PM2.5 exposure, the benefits of the increased habitual exercise outweighed the risks. Our findings suggest that habitual exercise is an effective approach for dyslipidemia prevention, even for people residing in relatively polluted areas.