Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Long-term variation of satellite-based PM2.5 and influence factors over East China.

ABSTRACT: With the explosive economic development of China over the past few decades, air pollution has attracted increasing global concern. Using satellite-based PM_2.5 data from 2000 to 2015, we found that the available emissions of atmospheric compositions show similar yearly variation trends to PM_2.5, even if the synchronization is not met for each composition, implying that the intensity of anthropogenic emissions dominates the temporal variation of PM_2.5 in East China. Empirical orthogonal function analysis demonstrates that the dominant variability in the seasonal PM_2.5 is closely associated with climate circulation transformation, incarnated as the specific climate index such as the Asia Polar Vortex intensity in spring, the Northern Hemisphere Subtropical High Ridge Position for the leading mode and the Kuroshio Current SST for the second mode in summer, the Asia Polar Vortex Area for the leading mode and the Pacific Polar Vortex Intensity for the second mode in autumn, the NINO A SSTA for the leading mode and the Pacific Decadal Oscillation for the second mode in winter. Therefore, apart from anthropogenic emissions effects, our results also provide robust evidence that over the past 16 years the climate factor has played a significant role in modulating PM_2.5 in eastern China.

SUBMITTER: He Q

PROVIDER: S-EPMC6078948 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Improvement of Surface PM2.5 Diurnal Variation Simulations in East Africa for the MAIA Satellite Mission

Project description: Not available

| S-EPMC11019548 | biostudies-literature

Association between satellite-based estimates of long-term PM2.5 exposure and coronary artery disease.

Project description:BACKGROUND:Epidemiological studies have identified associations between long-term PM2.5 exposure and cardiovascular events, though most have relied on concentrations from central-site air quality monitors. METHODS:We utilized a cohort of 5679 patients who had undergone cardiac catheterization at Duke University between 2002-2009 and resided in North Carolina. We used estimates of daily PM2.5 concentrations for North Carolina during the study period based on satellite derived Aerosol Optical Depth (AOD) measurements and PM2.5 concentrations from ground monitors, which were spatially resolved with a 10×10km resolution, matched to each patient's residential address and averaged for the year prior to catheterization. The Coronary Artery Disease (CAD) index was used to measure severity of CAD; scores >23 represent a hemodynamically significant coronary artery lesion in at least one major coronary vessel. Logistic regression modeled odds of having CAD or an MI with each 1?g/m(3) increase in annual average PM2.5, adjusting for sex, race, smoking status and socioeconomic status. RESULTS:In adjusted models, a 1?g/m(3) increase in annual average PM2.5 was associated with an 11.1% relative increase in the odds of significant CAD (95% CI: 4.0-18.6%) and a 14.2% increase in the odds of having a myocardial infarction (MI) within a year prior (95% CI: 3.7-25.8%). CONCLUSIONS:Satellite-based estimates of long-term PM2.5 exposure were associated with both coronary artery disease (CAD) and incidence of myocardial infarction (MI) in a cohort of cardiac catheterization patients.

| S-EPMC4706491 | biostudies-literature

Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004-2013.

Project description:Three decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ? 2.5 ?m) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data.We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations.We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations.The overall model cross-validation R(2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R(2) = 0.73, regression slope = 0.91) and seasonal (R(2) = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 ?g/m(3) between 2004 and 2007 and a decrease of 0.46 ?g/m(3) between 2008 and 2013.Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.

| S-EPMC4749081 | biostudies-other

The unidirectional causality influence of factors on PM2.5 in Shenyang city of China.

Project description:Air quality issue such as particulate matter pollution (PM2.5 and PM10) has become one of the biggest environmental problem in China. As one of the most important industrial base and economic core regions of China, Northeast China is facing serious air pollution problems in recent years, which has a profound impact on the health of local residents and atmospheric environment in some part of East Asia. Therefore, it is urgent to understand temporal-spatial characteristics of particles and analyze the causality factors. The results demonstrated that variation trend of particles was almost similar, the annual, monthly and daily distribution had their own characteristics. Particles decreased gradually from south to north, from west to east. Correlation analysis showed that wind speed was the most important factor affecting particles, and temperature, air pressure and relative humidity were key factors in some seasons. Path analysis showed that there was complex unidirectional causal relationship between particles and individual or combined effects, and NO2 and CO were key factors affecting PM2.5. The hot and cold areas changed little with the seasons. All the above results suggests that planning the industrial layout, adjusting industrial structure, joint prevention and control were necessary measure to reduce particles concentration.

| S-EPMC7242410 | biostudies-literature

Inland water bodies in China: Features discovered in the long-term satellite data.

Project description:Water bodies (WBs), such as lakes, ponds, and impoundments, provide essential ecosystem services for human society, yet their characteristics and changes over large areas remain elusive. Here we used unprecedented data layers derived from all Landsat images available between 1984 and 2015 to understand the overall characteristics and changes of WBs between 2 epochs (i.e., 1984 to 1999 and 2000 to 2015) in China. Results show that the abundance estimate of WBs greater than 1 km2 and the total WB surface area were 0.3 to 1.5 times and 0.2 to 0.5 times more than the previous estimates, respectively. The size-abundance and shoreline-area relationships of WBs in China conformed to the classic power scaling law, in contradiction to most previous studies. WB changes with various occurrence probabilities show widespread coexistence of disappearance of existent and emergence of new WBs across China driven primarily by human activities and climate change. Our results highlight the importance of using appropriate long-term satellite data to reveal the true properties and dynamics of WBs over large areas, which is essential for developing scaling theories and understanding the relative impacts of human activities and climate change on water resources in the world.

| S-EPMC6926064 | biostudies-literature

Long-term satellite tracking reveals variable seasonal migration strategies of basking sharks in the north-east Atlantic.

Project description:Animal migration is ubiquitous in nature with individuals within a population often exhibiting varying movement strategies. The basking shark (Cetorhinus maximus) is the world's second largest fish species, however, a comprehensive understanding of their long-term wider-ranging movements in the north-east Atlantic is currently lacking. Seventy satellite tags were deployed on basking sharks over four years (2012-2015) off the west coast of Scotland and the Isle of Man. Data from 28 satellite tags with attachment durations of over 165 days reveal post-summer ranging behaviours. Tagged sharks moved a median minimum straight-line distance of 3,633 km; achieving median displacement of 1,057 km from tagging locations. Tagged individuals exhibited one of three migration behaviours: remaining in waters of UK, Ireland and the Faroe Islands; migrating south to the Bay of Biscay or moving further south to waters off the Iberian Peninsula, and North Africa. Sharks used both continental shelf areas and oceanic habitats, primarily in the upper 50-200 m of the water column, spanning nine geo-political zones and the High Seas, demonstrating the need for multi-national cooperation in the management of this species across its range.

| S-EPMC5316944 | biostudies-literature

Satellite-Based Long-Term Spatiotemporal Patterns of Surface Ozone Concentrations in China: 2005-2019.

Project description:BackgroundAlthough short-term ozone (O3) exposure has been associated with a series of adverse health outcomes, research on the health effects of chronic O3 exposure is still limited, especially in developing countries because of the lack of long-term exposure estimates.ObjectivesThe present study aimed to estimate the spatiotemporal distribution of monthly mean daily maximum 8-h average O3 concentrations in China from 2005 to 2019 at a 0.05° spatial resolution.MethodsWe developed a machine learning model with a satellite-derived boundary-layer O3 column, O3 precursors, meteorological conditions, land-use information, and proxies of anthropogenic emissions as predictors.ResultsThe random, spatial, and temporal cross-validation R2 of our model were 0.87, 0.86, and 0.76, respectively. Model-predicted spatial distribution of ground-level O3 concentrations showed significant differences across seasons. The highest summer peak of O3 occurred in the North China Plain, whereas southern regions were the most polluted in winter. Most large urban centers showed elevated O3 levels, but their surrounding suburban areas may have even higher O3 concentrations owing to nitrogen oxides titration. The annual trend of O3 concentrations fluctuated over 2005-2013, but a significant nationwide increase was observed afterward.DiscussionThe present model had enhanced performance in predicting ground-level O3 concentrations in China. This national data set of O3 concentrations would facilitate epidemiological studies to investigate the long-term health effect of O3 in China. Our results also highlight the importance of controlling O3 in China's next round of the Air Pollution Prevention and Control Action Plan. https://doi.org/10.1289/EHP9406.

| S-EPMC8827621 | biostudies-literature

Spatial⁻Temporal Variations in NO₂ and PM2.5 over the Chengdu⁻Chongqing Economic Zone in China during 2005⁻2015 Based on Satellite Remote Sensing.

Project description:The Chengdu⁻Chongqing Economic Zone (CCEZ), which is located in southwestern China, is the fourth largest economic zone in China. The rapid economic development of this area has resulted in many environmental problems, including extremely high concentrations of nitrogen dioxide (NO₂) and fine particulate matter (PM2.5). However, current ground observations lack spatial and temporal coverage. In this study, satellite remote sensing techniques were used to analyze the variation in NO₂ and PM2.5 from 2005 to 2015 in the CCEZ. The Ozone Monitoring Instrument (OMI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product were used to retrieve tropospheric NO₂ vertical columns and estimate ground-level PM2.5 concentrations, respectively. Geographically, high NO₂ concentrations were mainly located in the northwest of Chengdu and southeast of Chongqing. However, high PM2.5 concentrations were mainly located in the center areas of the basin. The seasonal average NO₂ and PM2.5 concentrations were both highest in winter and lowest in summer. The seasonal average NO₂ and PM2.5 were as high as 749.33 × 1013 molecules·cm-2 and 132.39 µg·m-3 in winter 2010, respectively. Over 11 years, the annual average NO₂ and PM2.5 values in the CCEZ increased initially and then decreased, with 2011 as the inflection point. In 2007, the concentration of NO₂ reached its lowest value since 2005, which was 230.15 × 1013 molecules·cm-2, and in 2015, the concentration of PM2.5 reached its lowest value since 2005, which was 26.43 µg·m-3. Our study demonstrates the potential use of satellite remote sensing to compensate for the lack of ground-observed data when quantitatively analyzing the spatial⁻temporal variations in regional air quality.

| S-EPMC6264014 | biostudies-literature

Long-term influence of normal variation in neonatal characteristics on human brain development.

Project description:It is now recognized that a number of cognitive, behavioral, and mental health outcomes across the lifespan can be traced to fetal development. Although the direct mediation is unknown, the substantial variance in fetal growth, most commonly indexed by birth weight, may affect lifespan brain development. We investigated effects of normal variance in birth weight on MRI-derived measures of brain development in 628 healthy children, adolescents, and young adults in the large-scale multicenter Pediatric Imaging, Neurocognition, and Genetics study. This heterogeneous sample was recruited through geographically dispersed sites in the United States. The influence of birth weight on cortical thickness, surface area, and striatal and total brain volumes was investigated, controlling for variance in age, sex, household income, and genetic ancestry factors. Birth weight was found to exert robust positive effects on regional cortical surface area in multiple regions as well as total brain and caudate volumes. These effects were continuous across birth weight ranges and ages and were not confined to subsets of the sample. The findings show that (i) aspects of later child and adolescent brain development are influenced at birth and (ii) relatively small differences in birth weight across groups and conditions typically compared in neuropsychiatric research (e.g., Attention Deficit Hyperactivity Disorder, schizophrenia, and personality disorders) may influence group differences observed in brain parameters of interest at a later stage in life. These findings should serve to increase our attention to early influences.

| S-EPMC3523836 | biostudies-other

Impact of long-term temporal trends in fine particulate matter (PM2.5) on associations of annual PM2.5 exposure and mortality: An analysis of over 20 million Medicare beneficiaries.

Project description:Decreasing ambient fine particulate matter (PM2.5) concentrations over time together with increasing life expectancy raise concerns about temporal confounding of associations between PM2.5 and mortality. To address this issue, we examined PM2.5-associated mortality risk ratios (MRRs) estimated for approximately 20,000,000 US Medicare beneficiaries, who lived within six miles of an Environmental Protection Agency air quality monitoring site, between December 2000 and December 2012. We assessed temporal confounding by examining whether PM2.5-associated MRRs vary by study period length. We then evaluated three approaches to control for temporal confounding: (1) assessing exposures using the residual of PM2.5 regressed on time; (2) adding a penalized spline term for time to the health model; and (3) including a term that describes temporal variability in PM2.5 into the health model, with this term estimated using decomposition approaches. We found a 10 μg/m3 increase in PM2.5 exposure to be associated with a 1.20 times (95% confidence interval [CI] = 1.20, 1.21) higher risk of mortality across the 13-year study period, with the magnitude of the association decreasing with shorter study periods. MRRs remained statistically significant but were attenuated when models adjusted for long-term time trends in PM2.5. The residual-based, time-adjusted MRR equaled 1.12 (95% CI = 1.11, 1.12) per 10 μg/m3 for the 13-year study period and did not change when shorter study periods were examined. Spline- and decomposition-based approaches produced similar but less-stable MRRs. Our findings suggest that epidemiological studies of long-term PM2.5 can be confounded by long-term time trends, and this confounding can be controlled using the residuals of PM2.5 regressed on time.

| S-EPMC8493859 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data