Vertical variation of source-apportioned PM2.5 and selected volatile organic compounds near an elevated expressway in an urban area.

Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) are associated with adverse health effects and show spatial variation in three dimensions. The present study attempted to evaluate source contributions of PM2.5 and toxic VOCs in a metropolitan area focusing on the associated vertical variations. A special emphasis is put on the effects of the elevated expressway on the vertical variability of contribution estimates of the identified sources. Nine source factors, i.e., soil dust, sea salt/oil combustion, secondary nitrate, industrial emission, aged VOCs/secondary aerosol, traffic-related I, solvent use/industrial process, secondary sulfate, and traffic-related II, were identified using positive matrix factorization (PMF). The main contributors to PM2.5 were secondary sulfate (19.1%) and traffic-related emissions (traffic-related I and II, 16.1%), whereas the largest contributors to VOCs were traffic-related emissions (37.6%). The influence of the elevated expressway is suggested to be particularly critical on vertical variations of traffic-related emissions, including aging and secondary formation of locally accumulated air pollutants near roads. Increasing the building porosity under the viaduct could reduce the accumulation of air pollutants caused by the shelter effect. Additionally, in-street barriers would be beneficial in reducing population exposure to traffic-related emissions by altering the airflows near roads.

Associations of road traffic noise and its frequency spectrum with prevalent depression in Taichung, Taiwan.

Introduction: Exposure to road traffic noise has been reported to be associated with depression in many epidemiological studies, but the association between noise frequency spectrum and depression remains unclear. This community-based study investigated the associations between road traffic noise exposure and its frequency components with prevalent depression. Methods: A total of 3,191 residents living in Taichung who participated in the Taiwan Biobank between 2010 and 2017, were included as study participants. The land-use regression models were used to evaluate individual annual average values of A-weighted equivalent sound level over 24 h (Leq,24h) and particulate matter with an aerodynamic diameter <2.5 µm (PM2.5) using the geographic information system. Multiple logistic regression was applied to estimate the odds ratios (ORs) for depression after adjusting for potential risk factors and PM2.5. Results: An interquartile range increase in Leq,24h at full frequency (4.7 dBA), 1,000 Hz (5.2 dB), and 2,000 Hz (4.8 dB) was significantly associated with an elevated risk for depression with ORs of 1.62 (95% confidence interval [CI]: 1.03, 2.55), 1.58 (95% CI: 1.05, 2.37), and 1.58 (95% CI:1.03, 2.43), respectively, by controlling for PM2.5. The high-exposure group (≥3rd quartile median of noise levels) at full frequency, 1,000 Hz, and 2,000 Hz had an increased risk for depression with ORs of 2.65 (95% CI: 1.16-6.05), 2.47 (95% CI: 1.07-5.70), and 2.60 (95% CI: 1.10-6.12), respectively, compared with the reference group (<1st quartile of noise levels) after adjustment for PM2.5. Significant exposure-response trends were observed between the prevalent depression and noise exposure by quartiles at full frequency, 1,000 Hz, and 2,000 Hz (all p < 0.05). Conclusion: Exposure to road traffic noise may be associated with an increased prevalence of depression, particularly at 1,000 and 2,000 Hz.

Identifying and quantifying PM2.5 pollution episodes with a fusion method of moving window technique and constrained Positive Matrix Factorization.

PM2.5 pollution episodes rapidly and significantly deteriorate the air quality and are a critical concern worldwide. This study developed a fusion method based on the moving window dataset technique and constrained Positive Matrix Factorization (PMF) to differentiate and characterize potential factors in a PM2.5 episode case assuming having one new contributor. The hourly PM2.5 compositions of elements, ions and carbonaceous components, were collected from September to December 2020 in Taipei, Taiwan. Constraint targets based on the bootstrap analysis result of a PMF model using a long-term input dataset were imposed on the modeling of each moving window to ensure similar features of the retrieved factors. The constituents of an additionally differentiated factor to the episode, which was identified as regional transport, were stable among each moving window that covered the occurrence of the episode as revealed by the profile matching index. The results showed that the largest contributor to the PM2.5 mass during the episode period of 12/12/2020 was regional transport (61%), whereas that of 12/13 was the regular pollution of industry/ammonium sulfate related (43%). According to our review of the literature, this study is the first to apply both the moving window technique and constrained PMF to characterize the episode. The findings provide valuable information that can be used to explore the causes of PM2.5 episodes and implement air pollution control strategies.

Urban Air Pollution and Subclinical Atherosclerosis in Adolescents and Young Adults.

PURPOSE: The contribution of air pollution to subclinical atherosclerosis in a young population remains limited. This study aimed to assess whether long-term exposure to urban air pollutants increases carotid intima-media thickness (CIMT) in adolescents and young adults. METHODS: This study included 789 subjects between the ages of 12 and 30 years who lived in the Taipei metropolis from a cohort of young Taiwanese individuals. Residential addresses were geocoded, and annual average concentrations of particulate matter (PM) of different diameters, e.g., PM10, PM2.5-10, PM2.5, and nitrogen oxides (NOX), were assessed using land use regression models. The generalized least squares strategy with error term to consider the cluster effect of living addresses between individuals was used to examine the associations between urban air pollution and CIMTs. RESULTS: After adjusting for potential confounders, we found that interquartile range increases in PM2.5 (8.2 µg/m3) and NOX (17.5 µg/m3) were associated with 0.46% (95% CI: 0.02-0.90) and 1.00% (95% CI: 0.10-1.91) higher CIMTs, respectively. Stratified analyses showed that the relationships between CIMT and PM2.5 and NOX were more evident in subjects who were 18 years or older, female, nonsmoking, nonhypertensive, and nonhyperglycemic than in their respective counterparts. DISCUSSION: Long-term exposure to PM2.5 and NOX is associated with subclinical atherosclerosis in a young population. Age, sex, and health status may influence the vulnerability of air pollution-associated subclinical atherosclerosis.

Urban Fine Particulate Matter and Elements Associated with Subclinical Atherosclerosis in Adolescents and Young Adults.

The relationships between the elemental constituents of PM2.5 and atherosclerosis remain limited, especially in young populations. This study included 755 subjects aged 12-30 years in the Taipei metropolis. A land use regression model was used to estimate residential annual mean concentrations of PM2.5 and eight elemental constituents. We evaluated the percent differences in carotid intima-media thickness (CIMT) with PM2.5 and elemental constituent exposures by linear regressions. Interquartile range increments for PM2.5 (4.5 µg/m3), sulfur (108.6 ng/m3), manganese (2.0 ng/m3), iron (34.5 ng/m3), copper (3.6 ng/m3), and zinc (20.7 ng/m3) were found to associate with 0.92% (95% confidence interval (CI): 0.17-1.66), 0.51% (0.02-1.00), 0.36% (0.05-0.67), 0.98% (0.15-1.82), 0.74% (0.01-1.48), and 1.20% (0.33-2.08) higher CIMTs, respectively. Factor analysis identified four air pollution source-related factors, and the factors interpreted as traffic and industry sources were associated with higher CIMTs. Stratified analyses showed the estimates were more evident in subjects who were ≥18 years old, females, or who had lower household income. Our study results provide new insight into the impacts of source-specific air pollution, and future research on source-specific air pollution effects in young populations, especially in vulnerable subpopulations, is warranted.

Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities.

Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM2.5 has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM2.5 was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM2.5 in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM2.5 were obtained from the open-source land use database, OpenStreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM2.5 ranged from 13.51 to 15.40 µg/m³ among the cities. The two-stage models had the standard k-fold cross-validation (CV) R2 of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R2 of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM2.5 exposure could reach 32.1% with the distance increases by 37.8%. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.

Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach.

Fine particulate matter (PM2.5) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM2.5 measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM2.5 estimates in a study area. In this study, we used AOD estimates of surface PM2.5 together with meteorological and land use variables to estimate monthly PM2.5 concentrations at a spatial resolution of 3 km2 over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R2 of 0.98 with a root mean square error (RMSE) of 1.40 µg/m3. The leave-one-out cross-validation (LOOCV) R2 with seasonal stratification was 0.82, and the RMSE was 3.85 µg/m3, whereas the R2 and RMSE obtained by using the pure random forest approach produced R2 and RMSE values of 0.74 and 4.60 µg/m3, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM2.5 estimates over the entire island, which has complex terrain in terms of land use and topography.

Fatigue prevalence in men treated for prostate cancer: A systematic review and meta-analysis.

BACKGROUND: The side effects of prostate cancer (PCa) treatment are very prominent, with cancer-related fatigue (CRF) being the most common. Fatigue is a distressing symptom that interferes with daily functioning and seriously affects patient quality of life during, and for many years after, treatment. However, compared with other types of cancer, such as breast cancer, little is known about the prevalence of PCa-related fatigue. AIM: To determine the prevalence of CRF in patients with PCa. METHODS: A systematic search of EMBASE, PubMed, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure, WANFANG DATA, Technology Journal Database and the Chinese Biological Medical Database was conducted up to July 28, 2020. Included studies measured the incidence of PCa-related fatigue and differentiated fatigue outcomes (incidence) between treatment modalities and fatigue assessment times. In our meta-analysis, both fixed and random-effects models were used to estimate the pooled prevalence of PCa-related fatigue. Subgroup analyses were performed using treatment modalities and fatigue assessment times. Publication and sensitivity bias analyses were performed to test the robustness of the associations. RESULTS: Fourteen studies, involving 4736 patients, were eligible for the review. The pooled CRF prevalence was 40% in a total sample of 4736 PCa patients [95% confidence interval (CI): 29-52; P < 0.01; I 2 = 98%]. The results of the subgroup analyses showed the prevalence of CRF after androgen deprivation therapy treatment, radical prostatectomy and radiotherapy to be 42% (95%CI: 20-67, P < 0.01, I 2 = 91%), 21% (95%CI: 16-26, P = 0.87, I 2 = 0%) and 40% (95%CI: 22-58, P < 0.01, I 2 = 90%), respectively. The prevalence of acute and persistent fatigue was 44% (95%CI: 25-64; P < 0.01; I 2 = 93%) and 29% (95%CI: 25-32; P = 0.30; I 2 = 17%), respectively. CONCLUSION: Our meta-analysis showed that fatigue is a common symptom in men with PCa, especially those using hormone therapy.

Quantifying spatial heterogeneity of vulnerability to short-term PM2.5 exposure with data fusion framework.

The current estimations of the burden of disease (BD) of PM2.5 exposure is still potentially biased by two factors: ignorance of heterogeneous vulnerabilities at diverse urbanization levels and reliance on the risk estimates from existing literature, usually from different locations. Our objectives are (1) to build up a data fusion framework to estimate the burden of PM2.5 exposure while evaluating local risks simultaneously and (2) to quantify their spatial heterogeneity, relationship to land-use characteristics, and derived uncertainties when calculating the disease burdens. The feature of this study is applying six local databases to extract PM2.5 exposure risk and the BD information, including the risks of death, cardiovascular disease (CVD), and respiratory disease (RD), and their spatial heterogeneities through our data fusion framework. We applied the developed framework to Tainan City in Taiwan as a use case estimated the risks by using 2006-2016 emergency department visit data, air quality monitoring data, and land-use characteristics and further estimated the BD caused by daily PM2.5 exposure in 2013. Our results found that the risks of CVD and RD in highly urbanized areas and death in rural areas could reach 1.20-1.57 times higher than average. Furthermore, we performed a sensitivity analysis to assess the uncertainty of BD estimations from utilizing different data sources, and the results showed that the uncertainty of the BD estimations could be contributed by different PM2.5 exposure data (20-32%) and risk values (0-86%), especially for highly urbanized areas. In conclusion, our approach for estimating BD based on local databases has the potential to be generalized to the developing and overpopulated countries and to support local air quality and health management plans.

[Relationship between long non-coding RNA MALAT1 and prostate cancer].

Prostate cancer (PCa), as a malignant tumor originating in the prostate glandular epithelium, has become a global "killer" that threatens the health of elderly men. PCa-related studies have been focusing on the progression mechanisms and treatment strategies of the malignancy, particularly on the role of long non-coding RNA (lncRNA) in recent years. The lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) plays a key role in the progression and treatment of PCa, as well as in its metastasis and invasion and cell proliferation. lncRNA MALAT1 not only influences the biological characteristics of PCa, but also has a regulatory effect on the medicinal treatment of the disease, its action mechanisms involving ceRNA and AR signaling pathways. This review focuses on the relationship between lncRNA MALAT1 and PCa, aiming to provide a new research direction for the diagnosis and treatment of the malignancy.

Neuropathology changed by 3- and 6-months low-level PM2.5 inhalation exposure in spontaneously hypertensive rats.

BACKGROUND: Epidemiological evidence has linked fine particulate matter (PM2.5) to neurodegenerative diseases; however, the toxicological evidence remains unclear. The objective of this study was to investigate the effects of PM2.5 on neuropathophysiology in a hypertensive animal model. We examined behavioral alterations (Morris water maze), lipid peroxidation (malondialdehyde (MDA)), tau and autophagy expressions, neuron death, and caspase-3 levels after 3 and 6 months of whole-body exposure to urban PM2.5 in spontaneously hypertensive (SH) rats. RESULTS: SH rats were exposed to S-, K-, Si-, and Fe-dominated PM2.5 at 8.6 ± 2.5 and 10.8 ± 3.8 µg/m3 for 3 and 6 months, respectively. We observed no significant alterations in the escape latency, distance moved, mean area crossing, mean time spent, or mean swimming velocity after PM2.5 exposure. Notably, levels of MDA had significantly increased in the olfactory bulb, hippocampus, and cortex after 6 months of PM2.5 exposure (p < 0.05). We observed that 3 months of exposure to PM2.5 caused significantly higher expressions of t-tau and p-tau in the olfactory bulb (p < 0.05) but not in other brain regions. Beclin 1 was overexpressed in the hippocampus with 3 months of PM2.5 exposure, but significantly decreased in the cortex with 6 months exposure to PM2.5. Neuron numbers had decreased with caspase-3 activation in the cerebellum, hippocampus, and cortex after 6 months of PM2.5 exposure. CONCLUSIONS: Chronic exposure to low-level PM2.5 could accelerate the development of neurodegenerative pathologies in subjects with hypertension.

Trajectory-Assisted Source Apportionment of Winter-Time Aerosol Using Semi-continuous Measurements.

Receptor models, e.g., positive matrix factorization (PMF), are beneficial in designing effective control strategies to improve air quality. Additionally, integrating the trajectory analysis data into receptor modeling facilitates identifying the contributions from long-range transported aerosols. This study was conducted in Taipei City of Taiwan, a representative urban area with high population density, heavy traffic, and residential-commercial complexes. Hourly measurements were applied into an integrated trajectory-source apportionment approach. PMF was used to identify seven potential sources, including ammonium sulfate related, oil combustion, firework/firecracker, dust, vehicle, coal/marine, and industry/vehicle. Ammonium sulfate related source (33%) was characterized as the largest contributor, followed by coal/marine (18%) and industry/vehicle (16%). Through this integrated method, contribution estimates of the ammonium sulfate related factor from distant potential source regions were differentiated. Additionally, detailed distributions of source contributions to PM2.5 event periods were revealed by applying these highly time-resolved measurements.

Vertical distribution of source apportioned PM2.5 using particulate-bound elements and polycyclic aromatic hydrocarbons in an urban area.

Receptor models have been widely used for identifying and quantifying source-specific contributions from mixtures of air pollutants. Nonetheless, our knowledge is still limited on how various components of air pollution are vertically distributed and the sources of such pollutants. In this study, we collected 135 samples of PM2.5 (particles with aerodynamic diameter less than or equal to 2.5 µm) from building balconies at three altitudes in the metropolis of Taipei (Taiwan) and analyzed the samples for elements and polycyclic aromatic hydrocarbons that could be used to identify the sources of those pollutants. We used positive matrix factorization (PMF) to identify seven likely sources, including combustion, sulfur-rich aerosol, fresh traffic, industry/Cr-rich, oil combustion/vehicle, dust, and traffic. Although PM2.5 mass differed significantly between low-level and mid-level sites, the largest contributor to PM2.5 mass (sulfur-rich aerosol, 35.2%) showed nonsignificant variation in the vertical distribution. In contrast, oil combustion/vehicle, which exhibited significant difference between mid-level sites and the other two altitudes, might be a determinant in the vertical variation of PM2.5. We also observed negative trends with sampling height for combustion and traffic emissions.

Application of land-use regression models to estimate sound pressure levels and frequency components of road traffic noise in Taichung, Taiwan.

Few studies have applied land-use regression to predict road traffic noise exposure, and there are few predictive models for different frequencies. This study aimed to measure 24-h average road traffic noise levels and to analyze the frequency components over one year to establish land-use regression models of noise exposure. Fifty monitoring stations were set up to conduct 3 measurements for A-weighted equivalent sound pressure levels over 24 h (Leq,24h) and night equivalent sound pressure levels (Lnight), as well as octave-band analyses, during the 2013-2014 period. Noise measurements were integrated with land-use types, road and traffic information, meteorological data and geographic information systems to construct land-use regression models. Leave-one-out cross-validation was performed to test the validity of the predictive models. The annual means of Leq,24h and Lnight were 66.4 ±â€¯4.7 A-weighed decibels (dBA) and 62.1 ±â€¯6.0 dBA, respectively. Octave-band frequency analyses revealed that the highest means over 24 h and at night were 61.4 ±â€¯5.3 decibels (dB) and 56.7 ±â€¯6.6 dB (both at 1000 Hz), respectively. The model-explained variance (R2) of the full-frequency noise was 0.83 for Leq,24h and 0.79 for Lnight. The R2 values for octave-band-frequency noise ranged from 0.67 to 0.88 for Leq,24h and 0.65 to 0.85 for Lnight, with the highest R2 at 250 Hz for Leq,24h and at 125 Hz for Lnight. The differences between the model R2 and the leave-one-out cross-validation R2 ranged from 5% to 15% for both Leq,24h and Lnight at all frequencies. In the validation, the root mean squared error was 2.09 dBA and 2.80 dBA for the full-frequency Leq,24 and Lnight, respectively, and ranged from 1.89 to 2.62 dB and from 2.51 to 3.28 dB for the octave-band-frequency Leq,24h and Lnight, respectively. This study observed that the annual means of the measured Leq,24h and Lnight in Taichung were both above 60 dBA and had the highest level at 1000 Hz. The developed land-use regression models of Leq,24 and Lnight both had good predictive capacity for the full frequency spectrum and within octave bands and can therefore be applied for epidemiological studies.

Temperature-related mortality impacts under and beyond Paris Agreement climate change scenarios.

The Paris Agreement binds all nations to undertake ambitious efforts to combat climate change, with the commitment to Bhold warming well below 2 °C in global mean temperature (GMT), relative to pre-industrial levels, and to pursue efforts to limit warming to 1.5 °C". The 1.5 °C limit constitutes an ambitious goal for which greater evidence on its benefits for health would help guide policy and potentially increase the motivation for action. Here we contribute to this gap with an assessment on the potential health benefits, in terms of reductions in temperature-related mortality, derived from the compliance to the agreed temperature targets, compared to more extreme warming scenarios. We performed a multi-region analysis in 451 locations in 23 countries with different climate zones, and evaluated changes in heat and cold-related mortality under scenarios consistent with the Paris Agreement targets (1.5 and 2 °C) and more extreme GMT increases (3 and 4 °C), and under the assumption of no changes in demographic distribution and vulnerability. Our results suggest that limiting warming below 2 °C could prevent large increases in temperature-related mortality in most regions worldwide. The comparison between 1.5 and 2 °C is more complex and characterized by higher uncertainty, with geographical differences that indicate potential benefits limited to areas located in warmer climates, where direct climate change impacts will be more discernible.

Health effects of a forest environment on natural killer cells in humans: an observational pilot study.

Health effect assessments based on natural killer (NK) cells are an important emerging area of human health. We recruited 90 forest staff members in Xitou, Taiwan and 110 urban staff members in Taipei to investigate the health effects of forest environment exposure on NK cells (CD3-/CD56+) and activating NK cells (CD3-/CD56+/CD69+) in humans. We also invited 11 middle-aged volunteers in a pilot study to participate in a five-day/four-night forest trip to Xitou forest to investigate the health effects of a forest trip on NK cells and activating NK cells. Results showed that NK cells were higher in the forest group (19.5 ± 9.1%) than in the urban group (16.4 ± 8.4%). In particular, the percentage of NK cells was significantly higher in the forest group than in the urban group among the subgroups of male, a higher body mass index (≥ 25 kg/m2), without hypertension, lower high-sensitivity C-reactive protein, hyperglycemia, without smoking habit, and with tea drinking habit. After the five-day trip in Xitou forest, the percentage of activating NK cells of the invited participants from Taipei increased significantly after the trip to Xitou forest (0.83 ± 0.39% vs. 1.72 ± 0.1%). The percentage of activating NK cells was 1.13 ± 0.43%, which was higher than the baseline value of 0.77 ± 0.38% before the forest trip among the seven subjects who participated in the follow-up study four days after returning to Taipei. This study suggests that exposure to forest environments might enhance the immune response of NK cells and activating NK cells in humans.

Projections of temperature-related excess mortality under climate change scenarios.

BACKGROUND: Climate change can directly affect human health by varying exposure to non-optimal outdoor temperature. However, evidence on this direct impact at a global scale is limited, mainly due to issues in modelling and projecting complex and highly heterogeneous epidemiological relationships across different populations and climates. METHODS: We collected observed daily time series of mean temperature and mortality counts for all causes or non-external causes only, in periods ranging from Jan 1, 1984, to Dec 31, 2015, from various locations across the globe through the Multi-Country Multi-City Collaborative Research Network. We estimated temperature-mortality relationships through a two-stage time series design. We generated current and future daily mean temperature series under four scenarios of climate change, determined by varying trajectories of greenhouse gas emissions, using five general circulation models. We projected excess mortality for cold and heat and their net change in 1990-2099 under each scenario of climate change, assuming no adaptation or population changes. FINDINGS: Our dataset comprised 451 locations in 23 countries across nine regions of the world, including 85 879 895 deaths. Results indicate, on average, a net increase in temperature-related excess mortality under high-emission scenarios, although with important geographical differences. In temperate areas such as northern Europe, east Asia, and Australia, the less intense warming and large decrease in cold-related excess would induce a null or marginally negative net effect, with the net change in 2090-99 compared with 2010-19 ranging from -1·2% (empirical 95% CI -3·6 to 1·4) in Australia to -0·1% (-2·1 to 1·6) in east Asia under the highest emission scenario, although the decreasing trends would reverse during the course of the century. Conversely, warmer regions, such as the central and southern parts of America or Europe, and especially southeast Asia, would experience a sharp surge in heat-related impacts and extremely large net increases, with the net change at the end of the century ranging from 3·0% (-3·0 to 9·3) in Central America to 12·7% (-4·7 to 28·1) in southeast Asia under the highest emission scenario. Most of the health effects directly due to temperature increase could be avoided under scenarios involving mitigation strategies to limit emissions and further warming of the planet. INTERPRETATION: This study shows the negative health impacts of climate change that, under high-emission scenarios, would disproportionately affect warmer and poorer regions of the world. Comparison with lower emission scenarios emphasises the importance of mitigation policies for limiting global warming and reducing the associated health risks. FUNDING: UK Medical Research Council.

Longer-Term Impact of High and Low Temperature on Mortality: An International Study to Clarify Length of Mortality Displacement.

BACKGROUND: In many places, daily mortality has been shown to increase after days with particularly high or low temperatures, but such daily time-series studies cannot identify whether such increases reflect substantial life shortening or short-term displacement of deaths (harvesting). OBJECTIVES: To clarify this issue, we estimated the association between annual mortality and annual summaries of heat and cold in 278 locations from 12 countries. METHODS: Indices of annual heat and cold were used as predictors in regressions of annual mortality in each location, allowing for trends over time and clustering of annual count anomalies by country and pooling estimates using meta-regression. We used two indices of annual heat and cold based on preliminary standard daily analyses: a) mean annual degrees above/below minimum mortality temperature (MMT), and b) estimated fractions of deaths attributed to heat and cold. The first index was simpler and matched previous related research; the second was added because it allowed the interpretation that coefficients equal to 0 and 1 are consistent with none (0) or all (1) of the deaths attributable in daily analyses being displaced by at least 1 y. RESULTS: On average, regression coefficients of annual mortality on heat and cold mean degrees were 1.7% [95% confidence interval (CI): 0.3, 3.1] and 1.1% (95% CI: 0.6, 1.6) per degree, respectively, and daily attributable fractions were 0.8 (95% CI: 0.2, 1.3) and 1.1 (95% CI: 0.9, 1.4). The proximity of the latter coefficients to 1.0 provides evidence that most deaths found attributable to heat and cold in daily analyses were brought forward by at least 1 y. Estimates were broadly robust to alternative model assumptions. CONCLUSIONS: These results provide strong evidence that most deaths associated in daily analyses with heat and cold are displaced by at least 1 y. https://doi.org/10.1289/EHP1756.

Heat Wave and Mortality: A Multicountry, Multicommunity Study.

BACKGROUND: Few studies have examined variation in the associations between heat waves and mortality in an international context. OBJECTIVES: We aimed to systematically examine the impacts of heat waves on mortality with lag effects internationally. METHODS: We collected daily data of temperature and mortality from 400 communities in 18 countries/regions and defined 12 types of heat waves by combining community-specific daily mean temperature ≥90th, 92.5th, 95th, and 97.5th percentiles of temperature with duration ≥2, 3, and 4 d. We used time-series analyses to estimate the community-specific heat wave-mortality relation over lags of 0-10 d. Then, we applied meta-analysis to pool heat wave effects at the country level for cumulative and lag effects for each type of heat wave definition. RESULTS: Heat waves of all definitions had significant cumulative associations with mortality in all countries, but varied by community. The higher the temperature threshold used to define heat waves, the higher heat wave associations on mortality. However, heat wave duration did not modify the impacts. The association between heat waves and mortality appeared acutely and lasted for 3 and 4 d. Heat waves had higher associations with mortality in moderate cold and moderate hot areas than cold and hot areas. There were no added effects of heat waves on mortality in all countries/regions, except for Brazil, Moldova, and Taiwan. Heat waves defined by daily mean and maximum temperatures produced similar heat wave-mortality associations, but not daily minimum temperature. CONCLUSIONS: Results indicate that high temperatures create a substantial health burden, and effects of high temperatures over consecutive days are similar to what would be experienced if high temperature days occurred independently. People living in moderate cold and moderate hot areas are more sensitive to heat waves than those living in cold and hot areas. Daily mean and maximum temperatures had similar ability to define heat waves rather than minimum temperature. https://doi.org/10.1289/EHP1026.

Source apportionment of urban air pollutants using constrained receptor models with a priori profile information.

Exposure to air pollutants such as volatile organic compounds (VOCs) and fine particulate matter (PM2.5) are associated with adverse health effects. This study applied multiple time resolution data of hourly VOCs and 24-h PM2.5 to a constrained Positive Matrix Factorization (PMF) model for source apportionment in Taipei, Taiwan. Ninety-two daily PM2.5 samples and 2208 hourly VOC measurements were collected during four seasons in 2014 and 2015. With some a priori information, we used different procedures to constrain retrieved factors toward realistic sources. A total of nine source factors were identified as: natural gas/liquefied petroleum gas (LPG) leakage, solvent use/industrial process, contaminated marine aerosol, secondary aerosol/long-range transport, oil combustion, traffic related, evaporative gasoline emission, gasoline exhaust, and soil dust. Results showed that solvent use/industrial process was the largest contributor (19%) to VOCs while the largest contributor to PM2.5 mass was secondary aerosol/long-range transport (57%). A robust regression analysis showed that secondary aerosol was mostly contributed by regional transport related factor (25%).