Showing papers on "Air pollutant concentrations published in 2019"

Association Between Long-term Exposure to Ambient Air Pollution and Change in Quantitatively Assessed Emphysema and Lung Function

Abstract: Importance While air pollutants at historical levels have been associated with cardiovascular and respiratory diseases, it is not known whether exposure to contemporary air pollutant concentrations is associated with progression of emphysema. Objective To assess the longitudinal association of ambient ozone (O3), fine particulate matter (PM2.5), oxides of nitrogen (NOx), and black carbon exposure with change in percent emphysema assessed via computed tomographic (CT) imaging and lung function. Design, Setting, and Participants This cohort study included participants from the Multi-Ethnic Study of Atherosclerosis (MESA) Air and Lung Studies conducted in 6 metropolitan regions of the United States, which included 6814 adults aged 45 to 84 years recruited between July 2000 and August 2002, and an additional 257 participants recruited from February 2005 to May 2007, with follow-up through November 2018. Exposures Residence-specific air pollutant concentrations (O3, PM2.5, NOx, and black carbon) were estimated by validated spatiotemporal models incorporating cohort-specific monitoring, determined from 1999 through the end of follow-up. Main Outcomes and Measures Percent emphysema, defined as the percent of lung pixels less than −950 Hounsfield units, was assessed up to 5 times per participant via cardiac CT scan (2000-2007) and equivalent regions on lung CT scans (2010-2018). Spirometry was performed up to 3 times per participant (2004-2018). Results Among 7071 study participants (mean [range] age at recruitment, 60 [45-84] years; 3330 [47.1%] were men), 5780 were assigned outdoor residential air pollution concentrations in the year of their baseline examination and during the follow-up period and had at least 1 follow-up CT scan, and 2772 had at least 1 follow-up spirometric assessment, over a median of 10 years. Median percent emphysema was 3% at baseline and increased a mean of 0.58 percentage points per 10 years. Mean ambient concentrations of PM2.5and NOx, but not O3, decreased substantially during follow-up. Ambient concentrations of O3, PM2.5, NOx, and black carbon at study baseline were significantly associated with greater increases in percent emphysema per 10 years (O3: 0.13 per 3 parts per billion [95% CI, 0.03-0.24]; PM2.5: 0.11 per 2 μg/m3[95% CI, 0.03-0.19]; NOx: 0.06 per 10 parts per billion [95% CI, 0.01-0.12]; black carbon: 0.10 per 0.2 μg/m3[95% CI, 0.01-0.18]). Ambient O3and NOxconcentrations, but not PM2.5concentrations, during follow-up were also significantly associated with greater increases in percent emphysema. Ambient O3concentrations, but not other pollutants, at baseline and during follow-up were significantly associated with a greater decline in forced expiratory volume in 1 second per 10 years (baseline: 13.41 mL per 3 parts per billion [95% CI, 0.7-26.1]; follow-up: 18.15 mL per 3 parts per billion [95% CI, 1.59-34.71]). Conclusions and Relevance In this cohort study conducted between 2000 and 2018 in 6 US metropolitan regions, long-term exposure to ambient air pollutants was significantly associated with increasing emphysema assessed quantitatively using CT imaging and lung function.

Association of Long-term Exposure to Ambient Air Pollutants With Risk Factors for Cardiovascular Disease in China

Abstract: Importance Which cardiometabolic risk factors (eg, hypertension, type 2 diabetes, overweight or obesity, and dyslipidemia) are more sensitive to long-term exposure to ambient air pollution and whether participants with these conditions are more susceptible to the cardiovascular effects of air pollution remain unclear. Objectives To evaluate the associations among long-term exposure to air pollutants, cardiometabolic risk factors, and cardiovascular disease (CVD) prevalence. Design, Setting, and Participants This population-based cross-sectional study was conducted from April 1 through December 31, 2009, in 3 cities in Northeastern China. Participants were adults aged 18 to 74 years who had lived in study area for 5 years or longer. Data analysis was performed from May 1 through December 31, 2018. Exposures Long-term (2006-2008) exposure to air pollutants was measured using a spatiotemporal statistical model (particulate matter with an aerodynamic diameter of ≤2.5 μm [PM2.5] and ≤1.0 μm [PM1.0]) and data from air monitoring stations (particulate matter with an aerodynamic diameter of ≤10.0 μm [PM10.0], sulfur dioxide [SO2], nitrogen dioxide [NO2], and ozone [O3]). Main Outcomes and Measures Cardiovascular disease was determined by self-report of physician-diagnosed CVD. Blood pressure, body mass index, and levels of triglycerides and low-density lipoprotein cholesterol were measured using standard methods. Results Participants included 15 477 adults (47.3% women) with a mean (SD) age of 45.0 (13.5) years. The prevalence of CVD was 4.8%, and the prevalence of cardiometabolic risk factors ranged from 8.6% (hyperbetalipoproteinemia) to 40.5% (overweight or obesity). Mean (SD) air pollutant concentrations ranged from 35.3 (5.5) μg/m3(for NO2) to 123.1 (14.6) μg/m3(for PM10.0). Associations with air pollutants were identified for individuals with hyperbetalipoproteinemia (eg, odds ratio [OR], 1.36 [95% CI, 1.03-1.78] for a 10-μg/m3increase in PM1.0) and the weakest association for those with for overweight or obesity (eg, OR, 1.06 [95% CI, 1.02-1.09] for a 10-μg/m3increase in PM1.0). Cardiometabolic risk factors only partially mediated associations between air pollution and CVD. However, they modified the associations such that greater associations were found in participants with these cardiometabolic conditions (eg, ORs for CVD and per 10-μg/m3increase in PM1.0, 1.22 [95% CI, 1.12-1.33] in participants with hyperbetalipoproteinemia and 1.07 [95% CI, 0.98-1.16] in participants without hyperbetalipoproteinemia). Conclusions and Relevance In this population-based study of Chinese adults with CVD, long-term exposure to air pollution was associated with a higher prevalence of cardiometabolic risk factors, and the strongest associations were observed for hyperbetalipoproteinemia. In addition, participants with cardiometabolic risk factors may have been more vulnerable to the effects of air pollution on CVD.

Air pollution prediction by using an artificial neural network model

Abstract: Air pollutants impact public health, socioeconomics, politics, agriculture, and the environment. The objective of this study was to evaluate the ability of an artificial neural network (ANN) algorithm to predict hourly criteria air pollutant concentrations and two air quality indices, air quality index (AQI) and air quality health index (AQHI), for Ahvaz, Iran, over one full year (August 2009–August 2010). Ahvaz is known to be one of the most polluted cities in the world, mainly owing to dust storms. The applied algorithm involved nine factors in the input stage (five meteorological parameters, pollutant concentrations 3 and 6 h in advance, time, and date), 30 neurons in the hidden phase, and finally one output in last level. When comparing performance between using 5% and 10% of data for validation and testing, the more reliable results were from using 5% of data for these two stages. For all six criteria pollutants examined (O3, NO2, PM10, PM2.5, SO2, and CO) across four sites, the correlation coefficient (R) and root-mean square error (RMSE) values when comparing predictions and measurements were 0.87 and 59.9, respectively. When comparing modeled and measured AQI and AQHI, R2 was significant for three sites through AQHI, while AQI was significant only at one site. This study demonstrates that ANN has applicability to cities such as Ahvaz to forecast air quality with the purpose of preventing health effects. We conclude that authorities of urban air quality, practitioners, and decision makers can apply ANN to estimate spatial–temporal profile of pollutants and air quality indices. Further research is recommended to compare the efficiency and potency of ANN with numerical, computational, and statistical models to enable managers to select an appropriate toolkit for better decision making in field of urban air quality.

Effect of O3, PM10 and PM2.5 on cardiovascular and respiratory diseases in cities of France, Iran and Italy

Abstract: At present, both tropospheric ozone (O3) and particulate matters (PM) are among the most threatening air pollutants for human health in cities. The air pollution effects over public health include increased risk of hospital admissions and mortality for respiratory and cardiovascular diseases even when air pollutant concentrations are below European and international standards. The aim of this study was to (i) estimate the burden of mortality and morbidity for cardiovascular and respiratory diseases attributed to PM2.5, PM10 and O3 in nine selected cities in France, Iran and Italy in 2015 and 2016 and to (ii) compare estimated burdens at current O3 and PM levels with pre-industrial levels. The selected Mediterranean cities are among the most affected by the air pollution in Europe, in particular by rising O3 while the selected Iranian cities rank as the most polluted by PM in the world. The software AirQ+ was used to estimate the short-term health effects, in terms of mortality and morbidity by using in situ air quality data, city-specific relative risk values and baseline incidence. Compared to pre-industrial levels, long-term exposures to ambient PM2.5, PM10 and O3 have substantially contributed to mortality and hospital admissions in selected cities: about 8200 deaths for non-accidental causes, 2400 deaths for cardiovascular diseases, 540 deaths for respiratory diseases, 220 deaths for chronic obstructive pulmonary diseases as well as 18,800 hospital admissions for cardiovascular diseases and 3400 for respiratory diseases were reported in 2015. The study supports the need of city-specific epidemiological data and urgent strategies to mitigate the health burden of air pollution.

Acute effects of air pollutants on daily mortality and hospitalizations due to cardiovascular and respiratory diseases

Abstract: Background Chiang Dao is one of the districts in Chiang Mai, Thailand facing high level of seasonal air pollution every year, the exposure of community dwellers to outdoor air pollutants 24 hours a day during seasonal smog period because of their open-air housing style, and agricultural occupational hazard. In addition, Chiang Dao hospital is the only available hospital serving the community with open-air wards; therefore we could certainly to identify the association between air pollution and mortality of hospitalized patients. Thus, the aim of this study was to determine the association between daily average seasonal air pollutants and daily mortality of hospitalized patients and community dwellers as well as emergency and hospitalization visits for serious respiratory, cardiovascular, and cerebrovascular diseases. Methods This time series study was conducted between 1 March 2016 and 31 March 2017. The association of various air pollutant concentrations including particulate matter diameter less than 10 and 2.5 microns (PM10 and PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3) and daily mortality of hospitalized patients and community dwellers as well as relationship with frequencies of serious respiratory, cardiovascular, and cerebrovascular diseases were analyzed using a general linear model with Poisson distribution. Results Only PM2.5 was found to be associated with increased daily mortality of hospitalized patients (lag day 6, adjusted RR =1.153, 95% CI: 1.001-1.329), whereas PM10, PM2.5, NO2, and O3 were associated with increased daily non-accidental mortality of community dwellers (lag day 0-7, adjusted RR =1.006-1.040, 95% CI: 1.000-1.074). For acute serious respiratory events; PM10 and PM2.5 were associated with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), while SO2, CO, and O3 were associated with emergency visits for community-acquired pneumonia (CAP). O3 was associated with emergency visits for heart failure (HF), NO2 with emergency visits for myocardial infarction (MI), and SO2 with hospitalized visits for cerebrovascular accident (CVA). Conclusions Seasonal air pollutants were found to be associated with higher mortality among hospitalized patients and community dwellers with varying effects on severe acute respiratory, cardiovascular, and cerebrovascular diseases.

Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities—a case study in Sheffield

Abstract: Traditional real-time air quality monitoring instruments are expensive to install and maintain; therefore, such existing air quality monitoring networks are sparsely deployed and lack the measurement density to develop high-resolution spatiotemporal air pollutant maps. More recently, low-cost sensors have been used to collect high-resolution spatial and temporal air pollution data in real-time. In this paper, for the first time, Envirowatch E-MOTEs are employed for air quality monitoring as a case study in Sheffield. Ten E-MOTEs were deployed for a year (October 2016 to September 2017) monitoring several air pollutants (NO, NO2, CO) and meteorological parameters. Their performance was compared to each other and to a reference instrument installed nearby. E-MOTEs were able to successfully capture the temporal variability such as diurnal, weekly and annual cycles in air pollutant concentrations and demonstrated significant similarity with reference instruments. NO2 concentrations showed very strong positive correlation between various sensors. Mostly, correlation coefficients (r values) were greater than 0.92. CO from different sensors also had r values mostly greater than 0.92; however, NO showed r value less than 0.5. Furthermore, several multiple linear regression models (MLRM) and generalised additive models (GAM) were developed to calibrate the E-MOTE data and reproduce NO and NO2 concentrations measured by the reference instruments. GAMs demonstrated significantly better performance than linear models by capturing the non-linear association between the response and explanatory variables. The best GAM developed for reproducing NO2 concentrations returned values of 0.95, 3.91, 0.81, 0.005 and 0.61 for factor of two (FAC2), root mean square error (RMSE), coefficient of determination (R2), normalised mean biased (NMB) and coefficient of efficiency (COE), respectively. The low-cost sensors offer a more affordable alternative for providing real-time high-resolution spatiotemporal air quality and meteorological parameter data with acceptable performance.

Effects of ship emissions on air quality in the Baltic Sea region simulated with three different chemistry transport models

Abstract: . The Baltic Sea is a highly frequented shipping area with busy shipping lanes close to densely populated regions. Exhaust emissions from ship traffic into the atmosphere do not only enhance air pollution, they also affect the Baltic Sea environment through acidification and eutrophication of marine waters and surrounding terrestrial ecosystems. As part of the European BONUS project SHEBA (Sustainable Shipping and Environment of the Baltic Sea region), the transport, chemical transformation and fate of atmospheric pollutants in the Baltic Sea region were simulated with three regional chemistry transport model (CTM) systems, CMAQ, EMEP/MSC-W and SILAM, with grid resolutions between 4 and 11 km . The main goal was to quantify the effect that shipping emissions have on the regional air quality in the Baltic Sea region when the same shipping emission dataset but different CTMs are used in their typical set-ups. The performance of these models and the shipping contribution to the results of the individual models were evaluated for sulfur dioxide ( SO2 ), nitrogen dioxide ( NO2 ), ozone ( O3 ) and particulate matter ( PM2.5 ). Model results from the three CTMs for total air pollutant concentrations were compared to observations from rural and urban background stations of the AirBase monitoring network in the coastal areas of the Baltic Sea region. Observed PM2.5 in summer was underestimated strongly by CMAQ and to some extent by EMEP/MSC-W. Observed PM2.5 in winter was underestimated by SILAM. In autumn all models were in better agreement with observed PM2.5 . The spatial average of the annual mean O3 in the EMEP/MSC-W simulation was ca. 20 % higher compared to the other two simulations, which is mainly the consequence of using a different set of boundary conditions for the European model domain. There are significant differences in the calculated ship contributions to the levels of air pollutants among the three models. EMEP/MSC-W, with the coarsest grid, predicted weaker ozone depletion through NO emissions in the proximity of the main shipping routes than the other two models. The average contribution of ships to PM2.5 levels in coastal land areas is in the range of 3.1 %–5.7 % for the three CTMs. Differences in ship-related PM2.5 between the models are mainly attributed to differences in the schemes for inorganic aerosol formation. Differences in the ship-related elemental carbon ( EC ) among the CTMs can be explained by differences in the meteorological conditions, atmospheric transport processes and the applied wet-scavenging parameterizations. Overall, results from the present study show the sensitivity of the ship contribution to combined uncertainties in boundary conditions, meteorological data and aerosol formation and deposition schemes. This is an important step towards a more reliable evaluation of policy options regarding emission regulations for ship traffic and the planned introduction of a nitrogen emission control area (NECA) in the Baltic Sea and the North Sea in 2021.

Synoptic weather patterns and associated air pollution in Taiwan

Abstract: In this study, cluster analysis is applied to the daily averaged wind fields and sea-level pressure observed at surface weather stations in Taiwan from January 2013 to March 2018 to classify the synoptic weather patterns and study the characteristics of corresponding air pollutants, including fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone (O3). The classification identified six weather types: Clusters 1, 2, and 3 (C1–C3), which are typical winter weather types and associated with high air pollutant concentrations—C3, in particular, influenced by weak synoptic weather, is associated with the lowest wind speeds and the highest PM2.5 and PM10 concentrations and represents the most prevalent weather type that is prone to the occurrence of PM2.5 events; C4, which occurs mostly during seasonal transition months and is associated with the highest O3 concentrations; and C5 and C6, which are summer weather types with low air pollutant concentrations. Further analysis of the local wind flow using the 0.3° ERA5 reanalysis dataset and surface-observed wind data indicates that in western Taiwan, the land-sea breeze is embedded within the synoptic weather type of C3, which is favorable to air pollutant accumulation. However, when the prevailing northeasterly wind is obstructed by the Central Mountain Range, southwestern Taiwan, being situated on the leeside of the mountains, often exhibits the worst air pollution due to stagnant wind conditions.

The effects of roadside vegetation characteristics on local, near-road air quality

Abstract: Roadside vegetation has been shown to impact downwind, near-road air quality, with some studies identifying reductions in air pollution concentrations and others indicating increases in pollutant levels when vegetation is present. These widely contradictory results have resulted in confusion regarding the capability of vegetative barriers to mitigate near-road air pollution, which numerous studies have associated with significant adverse human health effects. Roadside vegetation studies have investigated the impact of many different types and conditions of vegetation barriers and urban forests, including preserved, existing vegetation stands usually consisting of mixtures of trees and shrubs or plantings of individual trees. A study was conducted along a highway with differing vegetation characteristics to identify if and how the changing characteristics affected downwind air quality. The results indicated that roadside vegetation needed to be of sufficient height, thickness, and coverage to achieve downwind air pollutant reductions. A vegetation stand which was highly porous and contained large gaps within the stand structure had increased downwind pollutant concentrations. These field study results were consistent with other studies that the roadside vegetation could lead to reductions in average, downwind pollutant concentrations by as much as 50% when this vegetation was thick with no gaps or openings. However, the presence of highly porous vegetation with gaps resulted in similar or sometimes higher concentrations than measured in a clearing with no vegetation. The combination of air quality and meteorological measurements indicated that the vegetation affects downwind pollutant concentrations through attenuation of meteorological and vehicle-induced turbulence as air passes through the vegetation, enhanced mixing as portions of the traffic pollution plume are blocked and forced over the vegetation, and through particulate deposition onto leaf and branch surfaces. Computational fluid dynamic modeling highlighted that density of the vegetation barrier affects pollutant levels, with a leaf area density of 3.0 m2 m-3 or higher needed to ensure downwind pollutant reductions for airborne particulate matter. These results show that roadside bushes and trees can be preserved or planted along highways and other localized pollution sources to mitigate air quality and human health impacts near the source if the planting adheres to important characteristics of height, thickness, and density with full coverage from the ground to the top of the canopy. The results also highlight the importance of planting denser vegetation and maintaining the integrity and structure of these vegetation barriers to achieve pollution reductions and not contribute to unintended increases in downwind air pollutant concentrations.

Modelling public health improvements as a result of air pollution control policies in the UK over four decades—1970 to 2010

Abstract: In much of the industrialised world, policy interventions to address the challenges of wide-spread air pollution as resulting from development and economic progress in the 2nd half of the 20th century have overall led to reductions in air pollution levels and related health effects since the 1970s. While overall improvements towards reducing health effects from ambient air pollution are recorded, comprehensive and consistent assessments of the long-term impact of policy interventions are still scarce. In this paper, we conduct a model assessment over a 40 year period of air pollution in the UK. In order to correct for the short and longer term variability of meteorological factors contributing to trends in ambient concentrations of priority air pollutants (nitrogen dioxide, sulphur dioxide, fine particulate matter and ozone), we use a fixed meteorological year for all model simulations. Hence, the modelled changes in air pollutant concentrations and related health effects are solely a function of the changes in emissions since 1970. These changes in emissions are primarily driven by policy interventions, ranging from phasing out of specific fuels or substances, to regulating the use of chemicals and driving the development of cleaner, more efficient technologies. Over the 40 year period, UK attributable mortality due to exposure to PM2.5 and NO2 have declined by 56% and 44% respectively, while ozone attributable respiratory mortality increased by 17% over the same period (however, with a slight decrease by 14% between 2000 and 2010).

The FireWork v2.0 air quality forecast system with biomass burning emissions from the Canadian Forest Fire Emissions Prediction System v2.03

Abstract: . Biomass burning activities can produce large quantities of smoke and result in adverse air quality conditions in regional environments. In Canada, the Environment and Climate Change Canada (ECCC) operational FireWork (v1.0) air quality forecast system incorporates near-real-time biomass burning emissions to forecast smoke plumes from fire events. The system is based on the ECCC operational Regional Air Quality Deterministic Prediction System (RAQDPS) augmented with near-real-time wildfire emissions using inputs from the Canadian Forest Service (CFS) Canadian Wildland Fire Information System (CWFIS). Recent improvements to the representation of fire behaviour and fire emissions have been incorporated into the CFS Canadian Forest Fire Emissions Prediction System (CFFEPS) v2.03. This is a bottom-up system linked to CWFIS in which hourly changes in biomass fuel consumption are parameterized with hourly forecasted meteorology at fire locations. CFFEPS has now also been connected to FireWork. In addition, a plume-rise parameterization based on fire-energy thermodynamics is used to define the smoke injection height and the distribution of emissions within a model vertical column. The new system, FireWork v2.0 (FireWork–CFFEPS), has been evaluated over North America for July–September 2017 and June–August 2018, which are both periods when western Canada experienced historical levels of fire activity with poor air quality conditions in several cities as well as other fires affecting northern Canada and Ontario. Forecast results were evaluated against hourly surface measurements for the three pollutant species used to calculate the Canadian Air Quality Health Index (AQHI), namely PM 2.5 , O3 , and NO2 , and benchmarked against the operational FireWork v1.0 system (FireWork-Ops). This comparison shows improved forecast performance and predictive skills for the FireWork–CFFEPS system. Modelled fire-plume injection heights from CFFEPS based on fire-energy thermodynamics show higher plume injection heights and larger variability. The changes in predicted fire emissions and injection height reduced the consistent over-predictions of PM 2.5 and O3 seen in FireWork-Ops. On the other hand, there were minimal fire emission contributions to surface NO2 , and results from FireWork–CFFEPS do not degrade NO2 forecast skill compared to the RAQDPS. Model performance statistics are slightly better for Canada than for the US, with lower errors and biases. The new system is still unable to capture the hourly variability of the observed values for PM 2.5 , but it captured the observed hourly variability for O3 concentration adequately. FireWork–CFFEPS also improves upon FireWork-Ops categorical scores for forecasting the occurrence of elevated air pollutant concentrations in terms of false alarm ratio (FAR) and critical success index (CSI).