Spatial interpolation of fine particulate matter concentrations using the shortest wind-field path distance.
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ABSTRACT:
SUBMITTER: Li L
PROVIDER: S-EPMC4010455 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundConsiderable attention has been paid to reproductive toxicity of fine particulate matter (PM2.5). However, the relationship between prenatal PM2.5 exposure and anogenital distance (AGD) has not been well studied. We aim to investigate the potential effects of prenatal exposure to PM2.5 on newborn AGD.MethodsPrenatal PM2.5 exposure of 2332 participates in Shanghai (2013-2016) was estimated using high-performance machine learning models. Anoscrotal distance (AGDas) in male infants and anofourchette distance (AGDaf) in female infants were measured by well-trained examiners within 3 days after birth. We applied multiple linear regression models and multiple informant models to estimate the association between prenatal PM2.5 exposure and AGD.ResultsMultiple linear regression models showed that a 10 μg/m3 increase in PM2.5 exposure during full pregnancy, the second and third trimesters was inversely associated with AGDas (adjusted beta = - 1.76, 95% CI: - 2.21, - 1.31; - 0.73, 95% CI: - 1.06, - 0.40; and - 0.52; 95% CI: - 0.87, - 0.18, respectively) in males. A 10 μg/m3 increase in PM2.5 exposure during the full pregnancy, the first, second, and third trimesters was inversely associated with AGDaf (adjusted beta = - 4.55; 95% CI: - 5.18, - 3.92; - 0.78; 95% CI: - 1.10, - 0.46; - 1.11; 95% CI: - 1.46, - 0.77; - 1.45; 95% CI: - 1.78, - 1.12, respectively) in females after adjusting for potential confounders. Multiple informant models showed consistent but slightly attenuated associations.ConclusionOur study observed a significant association between gestational PM2.5 exposure during pregnancy and shortened AGD in newborns, and provided new evidence on potential reproductive toxicity of prenatal PM2.5 exposure.
| S-EPMC9909868 | biostudies-literature
Project description:Background: Particulate matter pollution has become a growing health concern over the past few decades globally. The problem is especially evident in China, where particulate matter levels prior to 2013 are publically unavailable. We conducted a systematic review of scientific literature that reported fine particulate matter (PM2.5) concentrations in different regions of China from 2005 to 2016. Methods: We searched for English articles in PubMed and Embase and for Chinese articles in the China National Knowledge Infrastructure (CNKI). We evaluated the studies overall and categorized the collected data into six geographical regions and three economic regions. Results: The mean (SD) PM2.5 concentration, weighted by the number of sampling days, was 60.64 (33.27) ?g/m³ for all geographic regions and 71.99 (30.20) ?g/m³ for all economic regions. A one-way ANOVA shows statistically significant differences in PM2.5 concentrations between the various geographic regions (F = 14.91, p < 0.0001) and the three economic regions (F = 4.55, p = 0.01). Conclusions: This review identifies quantifiable differences in fine particulate matter concentrations across regions of China. The highest levels of fine particulate matter were found in the northern and northwestern regions and especially Beijing. The high percentage of data points exceeding current federal regulation standards suggests that fine particulate matter pollution remains a huge problem for China. As pre-2013 emissions data remain largely unavailable, we hope that the data aggregated from this systematic review can be incorporated into current and future models for more accurate historical PM2.5 estimates.
| S-EPMC5334745 | biostudies-literature
Project description:BACKGROUND: More than a decade of satellite observations offers global information about the trend and magnitude of human exposure to fine particulate matter (PM2.5). OBJECTIVE: In this study, we developed improved global exposure estimates of ambient PM2.5 mass and trend using PM2.5 concentrations inferred from multiple satellite instruments. METHODS: We combined three satellite-derived PM2.5 sources to produce global PM2.5 estimates at about 10 km × 10 km from 1998 through 2012. For each source, we related total column retrievals of aerosol optical depth to near-ground PM2.5 using the GEOS-Chem chemical transport model to represent local aerosol optical properties and vertical profiles. We collected 210 global ground-based PM2.5 observations from the literature to evaluate our satellite-based estimates with values measured in areas other than North America and Europe. RESULTS: We estimated that global population-weighted ambient PM2.5 concentrations increased 0.55 ?g/m3/year (95% CI: 0.43, 0.67) (2.1%/year; 95% CI: 1.6, 2.6) from 1998 through 2012. Increasing PM2.5 in some developing regions drove this global change, despite decreasing PM2.5 in some developed regions. The estimated proportion of the population of East Asia living above the World Health Organization (WHO) Interim Target-1 of 35 ?g/m3 increased from 51% in 1998-2000 to 70% in 2010-2012. In contrast, the North American proportion above the WHO Air Quality Guideline of 10 ?g/m3 fell from 62% in 1998-2000 to 19% in 2010-2012. We found significant agreement between satellite-derived estimates and ground-based measurements outside North America and Europe (r = 0.81; n = 210; slope = 0.68). The low bias in satellite-derived estimates suggests that true global concentrations could be even greater. CONCLUSIONS: Satellite observations provide insight into global long-term changes in ambient PM2.5 concentrations. Satellite-derived estimates and ground-based PM2.5 observations from this study are available for public use.
| S-EPMC4314252 | biostudies-literature
Project description:The data in this article provides route geometries and ultrafine particulate dosage information for a simulation of the home-to-school walking commute for children at 296,862 residential addresses in the city of Toronto, Canada. The datasets include dosage estimates that use a modelling approach that accounts for terrain, physiology and spatial variability in ambient UFP concentrations. The dataset provides simulated routes that describe both the shortest distance route, as well as the lowest UFP dosage route. Dosage and route information are provided in both polyline (route) and point (origin address) feature classes. Included in this article is a brief description of the simulation approach taken to generate the data. For discussion and complete description of the modelling approach, please refer to "Exposure to ultrafine particulate air pollution in the school commute: Examining low-dose route optimization with terrain-enforced dosage modelling" [1].
| S-EPMC6880117 | biostudies-literature
Project description:Several studies have pointed to fine particulate matter (PM2.5) as the main responsible for air pollution toxic effects. Indeed, PM2.5 may not only cause respiratory and cardiovascular abnormalities but it may also affect other organs such as the liver. Be that as it may, only a few studies have evaluated the PM2.5 effects on hepatic tissue. Moreover, most of them have not analyzed the relationship between particles composition and toxicological effects. In this study, healthy rats were subjected to urban levels of PM2.5 particles in order to assess their structural and functional effects on the liver. During the exposure periods, mean PM2.5 concentrations were slightly higher than the value suggested by the daily guideline of the World Health Organization. The exposed rats showed a hepatic increase of Cr, Zn, Fe, Ba, Tl and Pb levels. This group also showed leukocyte infiltration, sinusoidal dilation, hydropic inclusions and alterations in carbohydrates distribution. These histologic lesions were accompanied by serological changes, such as increase of total cholesterol and triglycerides, as well as genotoxic damage in their nuclei. We also observed significant associations between several biomarkers and PM2.5 composition. Our results show that exposure to low levels of PM2.5 might cause histologic and serological changes in liver tissue, suggesting that PM2.5 toxicity is influenced not only by their concentration but also by their composition and the exposure frequency.
| S-EPMC7099102 | biostudies-literature
Project description:We evaluate fine particulate matter (PM2.5) exposure-response models to propose a consistent set of global effect factors for product and policy assessments across spatial scales and across urban and rural environments. Relationships among exposure concentrations and PM2.5-attributable health effects largely depend on location, population density, and mortality rates. Existing effect factors build mostly on an essentially linear exposure-response function with coefficients from the American Cancer Society study. In contrast, the Global Burden of Disease analysis offers a nonlinear integrated exposure-response (IER) model with coefficients derived from numerous epidemiological studies covering a wide range of exposure concentrations. We explore the IER, additionally provide a simplified regression as a function of PM2.5 level, mortality rates, and severity, and compare results with effect factors derived from the recently published global exposure mortality model (GEMM). Uncertainty in effect factors is dominated by the exposure-response shape, background mortality, and geographic variability. Our central IER-based effect factor estimates for different regions do not differ substantially from previous estimates. However, IER estimates exhibit significant variability between locations as well as between urban and rural environments, driven primarily by variability in PM2.5 concentrations and mortality rates. Using the IER as the basis for effect factors presents a consistent picture of global PM2.5-related effects for use in product and policy assessment frameworks.
| S-EPMC6613786 | biostudies-literature
Project description:We numerically show that the statistical properties of the shortest path on critical percolation clusters are consistent with the ones predicted for Schramm-Loewner evolution (SLE) curves for ? = 1.04 ± 0.02. The shortest path results from a global optimization process. To identify it, one needs to explore an entire area. Establishing a relation with SLE permits to generate curves statistically equivalent to the shortest path from a Brownian motion. We numerically analyze the winding angle, the left passage probability, and the driving function of the shortest path and compare them to the distributions predicted for SLE curves with the same fractal dimension. The consistency with SLE opens the possibility of using a solid theoretical framework to describe the shortest path and it raises relevant questions regarding conformal invariance and domain Markov properties, which we also discuss.
| S-EPMC4074827 | biostudies-other
Project description:Specification of PM2.5 spatial and temporal characteristics is important for understanding PM2.5 adverse effects and policymaking. We applied network analysis to studying the dataset MIX, which contains PM2.5 emissions recorded from 2168 monitoring stations in China in 2008 and 2010. The results showed that for PM2.5 emissions from industrial sector 8 clusters were found in 2008 but they merged together into a huge cluster in 2010, suggesting that industrial sector underwent an integrating process. For PM2.5 emissions from electricity generation sector, strong locality of clusters was revealed, implying that each region had its own electricity generation system. For PM2.5 emissions from residential sector, the same pattern of 10 clusters was uncovered in both years, implicating the household energy consumption unchanged from 2008 to 2010. For PM2.5 emissions from transportation sector, the same pattern of 5 clusters with many connections in-between was unraveled, indicating the high-speed development of transportation nationalwidely. Except for the known elements, mercury (Hg) surfaced as an element for particle nucleation. To our knowledge, this is the first network study in this field.
| S-EPMC5016853 | biostudies-other
Project description:Short-term elevations in fine particulate matter air pollution (PM2.5) are associated with increased risk of acute cerebrovascular events. Evidence from the peripheral circulation suggests that vascular dysfunction may be a central mechanism. However, the effects of PM2.5 on cerebrovascular function and hemodynamics are unknown.We used transcranial Doppler ultrasound to measure beat-to-beat blood flow velocity in the middle cerebral artery at rest and in response to changes in end-tidal CO2 (cerebral vasoreactivity) and arterial blood pressure (cerebral autoregulation) in 482 participants from the Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly (MOBILIZE) of Boston study. We used linear mixed effects models with random subject intercepts to evaluate the association between cerebrovascular hemodynamic parameters and mean PM2.5 levels 1 to 28 days earlier adjusting for age, race, medical history, meteorologic covariates, day of week, temporal trends, and season.An interquartile range increase (3.0 µg/m(3)) in mean PM2.5 levels during the previous 28 days was associated with an 8.6% (95% confidence interval, 3.7%-13.8%; P<0.001) higher cerebral vascular resistance and a 7.5% (95% confidence interval, 4.2%-10.6%; P<0.001) lower blood flow velocity at rest. Measures of cerebral vasoreactivity and autoregulation were not associated with PM2.5 levels.In this cohort of community-dwelling seniors, exposure to PM2.5 was associated with higher resting cerebrovascular resistance and lower cerebral blood flow velocity. If replicated, these findings suggest that alterations in cerebrovascular hemodynamics may underlie the increased risk of particle-related acute cerebrovascular events.
| S-EPMC3722046 | biostudies-literature
Project description:Using daily fine particulate matter (PM2.5) composition data from the 2000-2005 U.S. EPA Chemical Speciation Network (CSN) for over 200 sites, we applied multivariate methods to identify and quantify the major fine particulate matter (PM2.5) source components in the U.S. Novel aspects of this work were: (1) the application of factor analysis (FA) to multi-city daily data, drawing upon both spatial and temporal variations of chemical species; and, (2) the exclusion of secondary components (sulfates, nitrates and organic carbon) from the source identification FA to more clearly discern and apportion the PM2.5 mass to primary emission source categories. For the quantification of source-related mass, we considered two approaches based upon the FA results: 1) using single key tracers for sources identified by FA in a mass regression; and, 2) applying Absolute Principal Component Analysis (APCA). In each case, we followed a two-stage mass regression approach, in which secondary components were first apportioned among the identified sources, and then mass was apportioned to the sources and to other secondary mass not explained by the individual sources. The major U.S. PM2.5 source categories identified via FA (and their key elements) were: Metals Industry (Pb, Zn); Crustal/Soil Particles (Ca, Si); Motor Vehicle Traffic (EC, NO2); Steel Industry (Fe, Mn); Coal Combustion (As, Se); Oil Combustion (V, Ni); Salt Particles (Na, Cl) and Biomass Burning (K). Nationwide spatial plots of the source-related PM2.5 impacts were confirmatory of the factor interpretations: ubiquitous sources, such as Traffic and Soil, were found to be spread across the nation, more unique sources (such as Steel and Metals Processing) being highest in select industrialized cities, Biomass Burning was highest in the U.S. Northwest, while Residual Oil combustion was highest in cities in the Northeastern U.S. and in cities with major seaports. The sum of these source contributions and the secondary PM2.5 components agreed well with the U.S. PM2.5 observed during the study period (mean=14.3 ug/m3; R2= 0.91). Apportionment regression analyses using single-element tracers for each source category gave results consistent with the APCA estimates. Comparisons of nearby sites indicated that the PM2.5 mass and the secondary aerosols were most homogenous spatially, while traffic PM2.5 and its tracer, EC, were among the most spatially representative of the source-related components. Comparison of apportionment results to a previous analysis of the 1979-1982 IP Network revealed similar and correlated major U.S. source category factors, albeit at lower levels than in the earlier period, suggesting a consistency in the U.S. spatial patterns of these source-related exposures over time, as well. These results indicate that applying source apportionment methods to the nationwide CSN can be an informative avenue for identifying and quantifying source components for the subsequent estimation of source-specific health effects, potentially contributing to more efficient regulation of PM2.5.
| S-EPMC3951912 | biostudies-literature