Dataset Information

Establishment of Regional Concentration-Duration-Frequency Relationships of Air Pollution: A Case Study for PM2.5.

ABSTRACT: Poor air quality usually leads to PM2.5 warnings and affects human health. The impact of frequency and duration of extreme air quality has received considerable attention. The extreme concentration of air pollution is related to its duration and annual frequency of occurrence known as concentration-duration-frequency (CDF) relationships. However, the CDF formulas are empirical equations representing the relationship between the maximum concentration as a dependent variable and other parameters of interest, i.e., duration and annual frequency of occurrence. As a basis for deducing the extreme CDF relationship of PM2.5, the function assumes that the extreme concentration is related to the duration and frequency. In addition, the spatial pattern estimation of extreme PM2.5 is identified. The regional CDF identifies the regional extreme concentration with a specified duration and return period. The spatial pattern of extreme air pollution over 8 h duration shows the hotspots of air quality in the central and southwestern areas. Central and southwestern Taiwan is at high risk of exposure to air pollution. Use of the regional CDF analysis is highly recommended for efficient design of air quality management and control.

SUBMITTER: Chu HJ

PROVIDER: S-EPMC7068585 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Establishment of Regional Concentration-Duration-Frequency Relationships of Air Pollution: A Case Study for PM<sub>2.5</sub>.

Chu Hone-Jay HJ Ali Muhammad Zeeshan MZ

International journal of environmental research and public health 20200222 4

Poor air quality usually leads to PM<sub>2.5</sub> warnings and affects human health. The impact of frequency and duration of extreme air quality has received considerable attention. The extreme concentration of air pollution is related to its duration and annual frequency of occurrence known as concentration-duration-frequency (CDF) relationships. However, the CDF formulas are empirical equations representing the relationship between the maximum concentration as a dependent variable and other p ...[more]

PMID: 32098343

Similar Datasets

Project description:The interactions between PM2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether spatial and seasonal variations exist deserves further research. In this study, the relationships between PM2.5 concentration and meteorological factors were investigated in 68 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM2.5 exist. Spatially, RH is positively correlated with PM2.5 concentration in north China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM2.5 everywhere except for Hainan Island. PS has a strong positive relationship with PM2.5 concentration in northeast China and mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM2.5 in autumn and the opposite in winter. PS is more positively correlated with PM2.5 in autumn than in other seasons. Our study investigated the relationships between PM2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM2.5 and meteorological factors are more comprehensive and precise than before. We suggest that the variations could be considered in PM2.5 concentration prediction and haze control to improve the prediction accuracy and policy efficiency.

Project description:Importance:Ambient fine particulate matter (PM2.5) air pollution is associated with increased risk of several causes of death. However, epidemiologic evidence suggests that current knowledge does not comprehensively capture all causes of death associated with PM2.5 exposure. Objective:To systematically identify causes of death associated with PM2.5 pollution and estimate the burden of death for each cause in the United States. Design, Setting, and Participants:In a cohort study of US veterans followed up between 2006 and 2016, ensemble modeling was used to identify and characterize morphology of the association between PM2.5 and causes of death. Burden of death associated with PM2.5 exposure in the contiguous United States and for each state was then estimated by application of estimated risk functions to county-level PM2.5 estimates from the US Environmental Protection Agency and cause-specific death rate data from the Centers for Disease Control and Prevention. Main Outcomes and Measures:Nonlinear exposure-response functions of the association between PM2.5 and causes of death and burden of death associated with PM2.5. Exposures:Annual mean PM2.5 levels. Results:A cohort of 4?522?160 US veterans (4?243?462 [93.8%] male; median [interquartile range] age, 64.1 [55.7-75.5] years; 3?702?942 [82.0%] white, 667?550 [14.8%] black, and 145?593 [3.2%] other race) was followed up for a median (interquartile range) of 10.0 (6.8-10.2) years. In the contiguous United States, PM2.5 exposure was associated with excess burden of death due to cardiovascular disease (56?070.1 deaths [95% uncertainty interval {UI}, 51?940.2-60?318.3 deaths]), cerebrovascular disease (40?466.1 deaths [95% UI, 21?770.1-46?487.9 deaths]), chronic kidney disease (7175.2 deaths [95% UI, 5910.2-8371.9 deaths]), chronic obstructive pulmonary disease (645.7 deaths [95% UI, 300.2-2490.9 deaths]), dementia (19?851.5 deaths [95% UI, 14?420.6-31?621.4 deaths]), type 2 diabetes (501.3 deaths [95% UI, 447.5-561.1 deaths]), hypertension (30?696.9 deaths [95% UI, 27?518.1-33?881.9 deaths]), lung cancer (17?545.3 deaths [95% UI, 15?055.3-20?464.5 deaths]), and pneumonia (8854.9 deaths [95% UI, 7696.2-10?710.6 deaths]). Burden exhibited substantial geographic variation. Estimated burden of death due to nonaccidental causes was 197?905.1 deaths (95% UI, 183?463.3-213?644.9 deaths); mean age-standardized death rates (per 100?000) due to nonaccidental causes were higher among black individuals (55.2 [95% UI, 50.5-60.6]) than nonblack individuals (51.0 [95% UI, 46.4-56.1]) and higher among those living in counties with high (65.3 [95% UI, 56.2-75.4]) vs low (46.1 [95% UI, 42.3-50.4]) socioeconomic deprivation; 99.0% of the burden of death due to nonaccidental causes was associated with PM2.5 levels below standards set by the US Environmental Protection Agency. Conclusions and Relevance:In this study, 9 causes of death were associated with PM2.5 exposure. The burden of death associated with PM2.5 was disproportionally borne by black individuals and socioeconomically disadvantaged communities. Effort toward cleaner air might reduce the burden of PM2.5-associated deaths.

Dataset Information

Establishment of Regional Concentration-Duration-Frequency Relationships of Air Pollution: A Case Study for PM2.5.

Publications

Establishment of Regional Concentration-Duration-Frequency Relationships of Air Pollution: A Case Study for PM<sub>2.5</sub>.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure