Ammonia emission abatement does not fully control reduced forms of nitrogen deposition.

Human activities and population growth have increased the natural burden of reactive nitrogen (N) in the environment. Excessive N deposition on Earth's surface leads to adverse feedbacks on ecosystems and humans. Similar to that of air pollution, emission control is recognized as an efficient means to control acid deposition. Control of nitrogen oxides (NOx = NO + NO2) emissions has led to reduction in deposition of oxidized nitrogen (NOy, the sum of all oxidized nitrogen species, except nitrous oxide [N2O]). Reduced forms of nitrogen (NHx = ammonia [NH3] + ammonium [NH4+]) deposition have, otherwise, increased, offsetting the benefit of reduction in NOy deposition. Stringent control of NH3 emissions is being considered. In this study, we assess the response of N deposition to N emission control on continental regions. We show that significant reduction of NHx deposition is unlikely to be achieved at the early stages of implementing NH3 emission abatement. Per-unit NH3 emission abatement is shown to result in only 60-80% reduction in NHx deposition, which is significantly lower than the demonstrated 80-120% benefit of controlling NOx emissions on NOy deposition. This 60-80% effectiveness of NHx deposition reduction per unit NH3 emission abatement reflects, in part, the effects of simultaneous reductions in NOx and SO2 emissions.

Improving Estimates of Sulfur, Nitrogen, and Ozone Total Deposition through Multi-Model and Measurement-Model Fusion Approaches.

Earth system and environmental impact studies need high quality and up-to-date estimates of atmospheric deposition. This study demonstrates the methodological benefits of multimodel ensemble and measurement-model fusion mapping approaches for atmospheric deposition focusing on 2010, a year for which several studies were conducted. Global model-only deposition assessment can be further improved by integrating new model-measurement techniques, including expanded capabilities of satellite observations of atmospheric composition. We identify research and implementation priorities for timely estimates of deposition globally as implemented by the World Meteorological Organization.

Understanding the role of anthropogenic emissions in glaciers retreat in the central Andes of Chile.

Glaciers in Chilean Central Andes have significatively retreated, at least, in the last 60 years. From 2004 to 2014, the largest retreat in the area (-0.15 km2 yr-1) was observed at Olivares Alpha Glacier (OAG). Previous glacier fluctuation studies proposed that two open-pit mines distant 7 km from the glacier could be the cause of its enhanced retreat. However, this had not been yet tested due to the lack of measured data. Here, we investigated the impact that major air pollutants emitted by local mining activities could have on the differences observed in OAG glacial retreat compared with a glacier of similar size and altitude with no nearby anthropogenic sources: Bello Glacier (BG), which has a reported lower retreat (-0.02 km2 yr-1). Results revealed a link between anthropogenic air pollutants and glacial retreat rates, meaning that glacial retreat is decoupled from climatic and glaciological factors. Considering that both glaciers are located in the same climatic setting, the anthropogenic air pollutants deposited onto the OAG surface appear to be forcing positive feedback in which the pollutants deposition best explain the differences in the glacier retreat. With the results of this study, it has been calculated that the impact of mining in OAG could be responsible for 82% of its total retreat since between 2004 and 2014, and only the remaining 18% would correspond to the impact of climate change.

Predicting U.S. Residential Building Energy Use and Indoor Pollutant Exposures in the Mid-21st Century.

The extent to which climate change and other factors will influence building energy use and population exposures to indoor pollutants is not well understood. Here, we develop and apply nationally representative residential energy and indoor pollutant model sets to estimate energy use, indoor pollutant concentrations, and associated chronic health outcomes across the U.S. residential building stock in the mid-21st century. The models incorporate expected changes in meteorological and ambient air quality conditions associated with IPCC RCP 8.5 and assumptions for changes in housing characteristics and population movements while keeping other less predictable factors constant. Site and source energy consumption for residential space-conditioning are predicted to decrease by ∼37-43 and ∼20-31%, respectively, in the 2050s compared to those in a 2010s reference scenario. Population-average indoor concentrations of pollutants of ambient origin are expected to decrease, except for O3. Holding indoor emission factors constant, indoor concentrations of pollutants with intermittent indoor sources are expected to decrease by <5% (PM2.5) to >30% (NO2); indoor concentrations of pollutants with persistent indoor sources (e.g., volatile organic compounds (VOCs)) are predicted to increase by ∼15-45%. We estimate negligible changes in disability-adjusted life-years (DALYs) lost associated with residential indoor pollutant exposures, well within uncertainty, although the attribution among pollutants is predicted to vary.

Estimation of future PM2.5- and ozone-related mortality over the continental United States in a changing climate: An application of high-resolution dynamical downscaling technique.

UNLABELLED: This paper evaluates the PM2.5- and ozone-related mortality at present (2000s) and in the future (2050s) over the continental United States by using the Environmental Benefits Mapping and Analysis Program (BenMAP-CE). Atmospheric chemical fields are simulated by WRF/CMAQ (horizontal resolution: 12×12 km), applying the dynamical downscaling technique from global climate-chemistry model under the Representative Concentration Pathways scenario (RCP 8.5). Future air quality results predict that the annual mean PM2.5 concentration in continental U.S. decreases nationwide, especially in the Eastern U.S. and west coast. However, the ozone concentration is projected to decrease in the Eastern U.S. but increase in the Western U.S. Future mortality is evaluated under two scenarios (1) holding future population and baseline incidence rate at the present level and (2) using the projected baseline incidence rate and population in 2050. For PM2.5, the entire continental U.S. presents a decreasing trend of PM2.5-related mortality by the 2050s in Scenario (1), primarily resulting from the emissions reduction. While in Scenario (2), almost half of the continental states show a rising tendency of PM2.5-related mortality, due to the dominant influence of population growth. In particular, the highest PM2.5-related deaths and the biggest discrepancy between present and future PM2.5-related deaths both occur in California in 2050s. For the ozone-related premature mortality, the simulation shows nation-wide rising tendency in 2050s under both scenarios, mainly due to the increase of ozone concentration and population in the future. Furthermore, the uncertainty analysis shows that the confidence interval of all causes mortality is much larger than that for specific causes, probably due to the accumulated uncertainty of generating datasets and sample size. The confidence interval of ozone-related all cause premature mortality is narrower than the PM2.5-related all cause mortality, due to its smaller standard deviation of the concentration-mortality response factor. IMPLICATIONS: The health impact of PM2.5 is more linearly proportional to the emission reductions than ozone. The reduction of anthropogenic PM2.5 precursor emissions is likely to lead to the decrease of PM2.5 concentrations and PM2.5 related mortality. However, the future ozone concentrations could increase due to increase of the greenhouse gas emissions of methane. Thus, to reduce the impact of ozone related mortality, anthropogenic emissions including criteria pollutant and greenhouse gas (i.e. methane) need to be controlled.

Design and demonstration of a next-generation air quality attainment assessment system for PM2.5 and O3.

Due to the increasingly stringent standards, it is important to assess whether the proposed emission reduction will result in ambient concentrations that meet the standards. The Software for Model Attainment Test-Community Edition (SMAT-CE) is developed for demonstrating attainment of air quality standards of O3 and PM2.5. SMAT-CE improves computational efficiency and provides a number of advanced visualization and analytical functionalities on an integrated GIS platform. SMAT-CE incorporates historical measurements of air quality parameters and simulated air pollutant concentrations under a number of emission inventory scenarios to project the level of compliance to air quality standards in a targeted future year. An application case study of the software based on the U.S. National Ambient Air Quality Standards (NAAQS) shows that SMAT-CE is capable of demonstrating the air quality attainment of annual PM2.5 and 8-hour O3 for a proposed emission control policy.

Development and case study of a science-based software platform to support policy making on air quality.

This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling (RSM) methodology and serves as a visualization and analysis tool (VAT) for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S. demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias <2% and assisting in air quality policy making in near real time.

Major factors influencing the health impacts from controlling air pollutants with nonlinear chemistry: an application to China.

Predicting the human-health effects of reducing atmospheric emissions of nitrogen oxide (NOx) emissions from power plants, motor vehicles, and other sources is complex because of nonlinearity in the relevant atmospheric processes. We estimate the health impacts of changes in fine particulate matter (PM2.5) and ozone concentrations that result from control of NOx emissions alone and in conjunction with other pollutants in and outside the mega-city of Shanghai, China. The Community Multiscale Air Quality (CMAQ) Modeling System is applied to model the effects on atmospheric concentrations of emissions from different economic sectors and geographic locations. Health impacts are quantified by combining concentration-response functions from the epidemiological literature with pollutant concentration and population distributions. We find that the health benefits per ton of emission reduction are more sensitive to the location (i.e., inside vs. outside of Shanghai) than to the sectors that are controlled. For eastern China, we predict between 1 and 20 fewer premature deaths per year per 1,000 tons of NOx emission reductions, valued at $300-$6,000 per ton. Health benefits are sensitive to seasonal variation in emission controls. Policies to control NOx emissions need to consider emission location, season, and simultaneous control of other pollutants to avoid unintended consequences.

Regional background ozone estimation for China through data fusion of observation and simulation.

Regional background ozone (O3_RBG) is an important component of surface ozone (O3). However, due to the uncertainties in commonly used Chemical Transport Models (CTMs) and statistical models, accurately assessing O3_RBG in China is challenging. In this study, we calculated the O3_RBG concentrations with the CTM - Brute Force Method (BFM) and constrained the results with site observations of O3 with the multiple linear regression (MLR) model. The annual average O3_RBG concentration in China region in 2020 is 35 ± 4 ppb, accounting for 81 ± 5 % of the maximum 8-h average O3 (MDA8 O3). We applied the random forest and Shapley additive explanations based on meteorological standardization techniques to separate the contributions of meteorology and natural emissions to O3_RBG. Natural emissions contribute more significantly to O3_RBG than meteorology in various Chineses regions (30-40 ppb), with higher contributions during the warm season. Meteorological factors show higher contributions in the spring and summer seasons (2-3 ppb) than the other seasons. Temperature and humidity are the primary contributors to O3_RBG in regions with severe O3 pollution in China, with their individual impacts ranging from 30 % to 62 % of the total impacts of all meteorological factors in different seasons. For policy implications, we tracked the contributions of O3_RBG and local photochemical reaction contributions (O3_LC) to total O3 concentration at different O3 levels. We found that O3_LC contribute over 45 % to MDA8 O3 on polluted days, supporting the current Chinese policy of reducing O3 peak concentrations by cutting down precursor emissions. However, as the contribution of O3_RBG is not considered in the policy, additional efforts are needed to achieve the control groal of O3 concentration. As the implementation of stringent O3 control measurements in China, the contribution of O3_RBG become increasingly significant, suggesting the need for attention to O3_RBG and regional joint prevention and control.

Ozone response modeling to NOx and VOC emissions: Examining machine learning models.

Current machine learning (ML) applications in atmospheric science focus on forecasting and bias correction for numerical modeling estimations, but few studies examined the nonlinear response of their predictions to precursor emissions. This study uses ground-level maximum daily 8-hour ozone average (MDA8 O3) as an example to examine O3 responses to local anthropogenic NOx and VOC emissions in Taiwan by Response Surface Modeling (RSM). Three different datasets for RSM were examined, including the Community Multiscale Air Quality (CMAQ) model data, ML-measurement-model fusion (ML-MMF) data, and ML data, which respectively represent direct numerical model predictions, numerical predictions adjusted by observations and other auxiliary data, and ML predictions based on observations and other auxiliary data. The results show that both ML-MMF (r = 0.93-0.94) and ML predictions (r = 0.89-0.94) present significantly improved performance in the benchmark case compared with CMAQ predictions (r = 0.41-0.80). While ML-MMF isopleths exhibit O3 nonlinearity close to actual responses due to their numerical base and observation-based correction, ML isopleths present biased predictions concerning their different controlled ranges of O3 and distorted O3 responses to NOx and VOC emission ratios compared with ML-MMF isopleths, which implies that using data without support from CMAQ modeling to predict the air quality could mislead the controlled targets and future trends. Meanwhile, the observation-corrected ML-MMF isopleths also emphasize the impact of transboundary pollution from mainland China on the regional O3 sensitivity to local NOx and VOC emissions, which transboundary NOx would make all air quality regions in April more sensitive to local VOC emissions and limit the potential effort by reducing local emissions. Future ML applications in atmospheric science like forecasting or bias correction should provide interpretability and explainability, except for meeting statistical performance and providing variable importance. Assessment with interpretable physical and chemical mechanisms and constructing a statistically robust ML model should be equally important.

Sensitivity and linearity analysis of ozone in East Asia: the effects of domestic emission and intercontinental transport.

UNLABELLED: In this study, ozone (O3) sensitivity and linearity over East Asia (EA) and seven urban areas are examined with an integrated air quality modeling system under two categories of scenarios: (1) The effects of domestic emission are estimated under local emission reduction scenarios, as anthropogenic NO(x) and volatile organic compounds (VOC) emissions are reduced by 20%, 50%, and 100%, respectively and independently; and (2) the influence of intercontinental transport is evaluated under Task Force on Hemispheric Transport of Air Pollution (TF HTAP) emission reduction scenarios, as anthropogenic NO(x) emission is reduced by 20% in Europe (EU), North America (NA), and South Asia (SA), respectively. Simulations are conducted for January and July 2001 to examine seasonal variation. Through the domestic O3 sensitivity investigation, we find O3 sensitivity varies dynamically depending on both time and location: North EA is VOC limited in January and NO(x) limited in July, except for the urban areas Beijing, Shanghai, Tokyo, and Seoul, which are VOC limited in both months; south EA is NO(x) limited in both January and July, except for the urban areas Taipei, which is VOC-limited in both months, and Pearl River Delta, which is VOC limited in January. Surface O3 change is found to be affected more by NO(x) than by VOC over EA in both January and July. We also find different O3 linearity characteristics among urban areas in EA: O3 at Beijing, Tokyo, and Seoul shows a strong negative linear response to NO(x) emission in January; O3 at Shanghai, Pearl River Delta, and Taipei shows a strong positive response to VOC emission in both January and July. Through the long-range transport investigation, monthly O3 changes over EA resulting from different source regions indicate the largest source contribution comes from NA (0.23 ppb), followed by SA (0.11 ppb) and EU (0.10 ppb). All of the three regions show higher impacts in January than in July. IMPLICATIONS: This study examine O3 sensitivities and linear response of NO(x) and VOC emission over EA and seven urban areas based on regional air quality modeling system MM5/CMAQ. We also quantify the intercontinental transport effect from EU, SA, and NA over EA. The result provide a theoretical basis for emission control strategy design in EA, and also reveal the O3 special nonlinearity features for further related studies that are applicable to other continents. The HTAP multimodel experiments need to examine the potential impacts on ground-level O3 of changes in meteorology and transport patterns expected as a result of the regional scale.

Projections of future wildfires impacts on air pollutants and air toxics in a changing climate over the western United States.

Wildfires emit smoke particles and gaseous pollutants that greatly aggravate air quality and cause adverse health impacts in the western US (WUS). This study evaluates how wildfire impacts on air pollutants and air toxics evolve from the present climate to the future climate under a high anthropogenic emission scenario at regional and city scales. Through employing multiple climate and chemical transport models, small changes in domain-averaged air pollutant concentrations by wildfires are simulated over WUS. However, such changes significantly increase future city-scale pollutant concentrations by up to 53 ppb for benzene, 158 ppb for formaldehyde, 655 µg/m3 for fine particulate matter (PM2.5), and 102 ppb for ozone, whereas that for the present climate are 104 ppb for benzene, 332 ppb for formaldehyde, 1,378 µg/m3 for PM2.5, and 140 ppb for ozone. Despite wildfires induce smaller changes in the future, the wildfire contribution ratios can increase by more than tenfold compared to the present climate, indicating wildfires become a more critical contributor to future air pollution in WUS. In addition, additional 6 exceedance days/year for formaldehyde and additional 3 exceedance days/year for ozone suggest increasing health impacts by wildfires in the future.

Localized energy burden, concentrated disadvantage, and the feminization of energy poverty.

Energy burden directly influences households' health and safety. Amid a growing literature on energy, poverty and gender remains relatively understudied. We evaluate socioeconomic, geographic, and health factors as multidimensions of concentrated disadvantage that magnify energy burden in the United States over time. We show that the energy burden is more pronounced in disadvantaged counties with larger elderly, impoverished, disabled people, and racialized populations where people do not have health insurance. Neighborhoods with households headed by women of color (especially Black women) are more likely to face a high energy burden, which worsened during the COVID-19 pandemic. Although energy costs are often regarded as an individual responsibility, these findings illustrate the feminization of energy poverty and indicate the need for an intersectional and interdisciplinary framework in devising energy policy directed to households with the most severe energy burden.

Quantifying the wet deposition of reactive nitrogen over China: Synthesis of observations and models.

Accurate estimation on reaction nitrogen (Nr) deposition is highly demanded for assessing the impacts on the environment and human beings. This study investigated the wet deposition of inorganic nitrogen (IN) in mainland China by measurements from over 500 sites from five observational networks/databases and ensemble results of eleven chemical transport models (CTMs). Each data source has its focus and limitations and together formed a comprehensive view over China. But the inconsistency among different sources may hinder the appropriate usage of data. Model evaluation results demonstrated the models' deficiency in simulating the wet NO3- deposition over Southeast China (40 % underestimation) and showed an overall underestimation of wet NH4+ deposition over the hotspot regions (5-60 % underestimation). A synthesis of this study and twelve reference studies was conducted to quantify the national amount of wet IN deposition. The estimations by CTMs ranged 2.4-3.9 Tg(N) yr-1 for wet NOy deposition and 4-6.7 Tg(N) yr-1 for wet NHx deposition, after adjusting the results with 10-19 % underestimations in wet NOy deposition and 1-40 % underestimations in wet NHx deposition. The estimations by ground observations ranged 7.1-9 Tg(N) yr-1 for wet NOy deposition and 8-13.1 Tg(N) yr-1 for wet NHx deposition, which were 20-275 % higher than the estimation by CTMs, but the results were strongly influenced by the abundances and representative of measurements. Studies using statistical techniques to interpolate site observations predicted 3-5.5 Tg(N) yr-1 for wet NOy deposition and 3.9-7.2 Tg(N) yr-1 for wet NHx deposition. This approach benefited from high accuracy and good robustness of the statistical models, but the uncertainty in the interpolation methods could be a potential drawback.

Impact assessment of ammonia emissions on inorganic aerosols in East China using response surface modeling technique.

Ammonia (NH(3)) is one important precursor of inorganic fine particles; however, knowledge of the impacts of NH(3) emissions on aerosol formation in China is very limited. In this study, we have developed China's NH(3) emission inventory for 2005 and applied the Response Surface Modeling (RSM) technique upon a widely used regional air quality model, the Community Multi-Scale Air Quality Model (CMAQ). The purpose was to analyze the impacts of NH(3) emissions on fine particles for January, April, July, and October over east China, especially those most developed regions including the North China Plain (NCP), Yangtze River delta (YRD), and the Pearl River delta (PRD). The results indicate that NH(3) emissions contribute to 8-11% of PM(2.5) concentrations in these three regions, comparable with the contributions of SO(2) (9-11%) and NO(x) (5-11%) emissions. However, NH(3), SO(2), and NO(x) emissions present significant nonlinear impacts; the PM(2.5) responses to their emissions increase when more control efforts are taken mainly because of the transition between NH(3)-rich and NH(3)-poor conditions. Nitrate aerosol (NO(3)(-)) concentration is more sensitive to NO(x) emissions in NCP and YRD because of the abundant NH(3) emissions in the two regions, but it is equally or even more sensitive to NH(3) emissions in the PRD. In high NO(3)(-) pollution areas such as NCP and YRD, NH(3) is sufficiently abundant to neutralize extra nitric acid produced by an additional 25% of NO(x) emissions. The 90% increase of NH(3) emissions during 1990-2005 resulted in about 50-60% increases of NO(3)(-) and SO(4)(2-) aerosol concentrations. If no control measures are taken for NH(3) emissions, NO(3)(-) will be further enhanced in the future. Control of NH(3) emissions in winter, spring, and fall will benefit PM(2.5) reduction for most regions. However, to improve regional air quality and avoid exacerbating the acidity of aerosols, a more effective pathway is to adopt a multipollutant strategy to control NH(3) emissions in parallel with current SO(2) and NO(x) controls in China.

Evaluating the impact of mobility on COVID-19 pandemic with machine learning hybrid predictions.

COVID-19 pandemic had expanded to the US since early 2020 and has caused nationwide economic loss and public health crisis. Until now, although the US has the most confirmed cases in the world and are still experiencing an increasing pandemic, several states insisted to re-open business activities and colleges while announced strict control measures. To provide a quantitative reference for official strategies, predicting the near future trend based on finer spatial resolution data and presumed scenarios are urgently needed. In this study, the first attempted COVID-19 case predicting model based on county-level demographic, environmental, and mobility data was constructed with multiple machine learning techniques and a hybrid framework. Different scenarios were also applied to selected metropolitan counties including New York City, Cook County in Illinois, Los Angeles County in California, and Miami-Dade County in Florida to assess the impact from lockdown, Phase I, and Phase III re-opening. Our results showed that, for selected counties, the mobility decreased substantially after the lockdown but kept increasing with an apparent weekly pattern, and the weekly pattern of mobility and infections implied high infections during the weekend. Meanwhile, our model was successfully built up, and the scenario assessment results indicated that, compared with Phase I re-opening, a 1-week and a 2-week lockdown could reduce 4%-29% and 15%-55% infections, respectively, in the future week, while 2-week Phase III re-opening could increase 16%-80% infections. We concluded that the mandatory orders in metropolitan counties such lockdown should last longer than one week, the effect could be observed. The impact of lockdown or re-opening was also county-dependent and varied with the local pandemic. In future works, we expect to involve a longer period of data, consider more county-dependent factors, and employ more sophisticated techniques to decrease the modeling uncertainty and apply it to counties nationally and other countries.

The black carbon dispersion in the Southern Hemisphere and its transport and fate to Antarctica, an Anthropocene evidence for climate change policies.

Black carbon (BC) has been measured in Antarctica's air, and its global warming effect can potentially speed up the ice melting in the most solid water reservoir of the planet. However, the primary responsible sources are not well evidenced in this region. The dispersion of black carbon emissions from the Southern Hemisphere was conducting using atmospheric chemical transport model and we compared the results with satellite registries from March 1st to April 30th in 2014. The emission inventory considered the anthropogenic and biomass burning emissions from global datasets. The largest and most populated cities in Southern Hemisphere showed the higher emission of BC. As a result, the average daily concentrations of atmospheric BC were around 4 ng/m3 in most regions of Antarctica according to its pristine characteristics. We analyzed fifteen relevant sites in coastal zones of Antartica and some peaks registered by the satellite records were not replicated by model outputs and it was mainly associated with the lack of emissions. Finally, we made simulations in the same period without biomass burning emissions and we observed decreased concentrations of BC in the range of 20-50%. As a result, we show that the black carbon transportation from the continental land to the polar region took place in 17-24 days during the Austral summer and the biomass burning emissions were the primary source. Black Carbon deposition in Antarctica is not permanent, but the uncontrolled emissions from Southern Hemisphere can increase its transportation to the white continent and make its accumulation during the period when the weak polar vortex occurs.

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.

Transient risk of ambient fine particulate matter on hourly cardiovascular events in Tainan City, Taiwan.

BACKGROUND: The association between daily changes in ambient fine particulate matter (PM2.5) and cardiovascular diseases have been well established in mechanistic, epidemiologic and exposure studies. Only a few studies examined the effect of hourly variations in air pollution on triggering cardiovascular events. Whether the current PM2.5 standards can protect vulnerable individuals with chronic cardiovascular diseases remain uncertain. METHODS: we conducted a time-stratified, case-crossover study to assess the associations between hourly changes in PM2.5 levels and the vascular disease onset in residents of Tainan City, Taiwan, visiting Emergency Room of Chi Mei Medical Center between January 2006 and December 2016. There were 26,749 cases including 10,310 females (38.5%) and 16,439 males (61.5%) identified. The time of emergency visit was identified as the onset for each case and control cases were selected as the same times on other days, on the same day of the week in the same month and year respectively. Residential address was used to identify the ambient air pollution exposure concentrations from the closest station. Conditional logistic regression with the stepwise selection method was used to estimate adjusted odds ratios (ORs) for the association. RESULTS: When we only included cases occurring at PM2.5>10 µg/m3 and PM2.5>25 µg/m3, very significant ORs could be observed for 10 µg/m3 increases in PM2.5 at 0 and 1 hour, implying fine particulate exposure could promptly trigger vascular disease events. Moreover, a very clear increase in risk could be observed with cumulative exposure from 0 to 48 hours, especially in those cases where PM2.5>25 µg/m3. CONCLUSIONS: Our study demonstrated that transient and low concentrations of ambient PM2.5 trigger adult vascular disease events, especially cerebrovascular disease, regardless of age, sex, and exposure timing. Warning and delivery systems should be setup to protect people from these prompt adverse health impacts.