Long-term Average PM2 Research Articles

Indirect Effects of PM2.5 Exposure on COVID-19 Mortality in Greater Jakarta, Indonesia: An Ecological Study.

Air pollution, including PM2.5, was suggested as one of the primary contributors to COVID-19 fatalities worldwide. Jakarta, the capital city of Indonesia, was recognized as one of the ten most polluted cities globally. Additionally, the incidence of COVID-19 in Jakarta surpasses that of all other provinces in Indonesia. However, no study has investigated the correlation between PM2.5 concentration and COVID-19 fatality in Jakarta. To investigate the correlation between short-term and long-term exposure to PM2.5 and COVID-19 mortality in Greater Jakarta area. An ecological time-trend study was implemented. The data of PM2.5 ambient concentration obtained from Nafas Indonesia and the National Institute for Aeronautics and Space (LAPAN)/National Research and Innovation Agency (BRIN). The daily COVID-19 death data obtained from the City's Health Office. Our study unveiled an intriguing pattern: while short-term exposure to PM2.5 showed a negative correlation with COVID-19 mortality, suggesting it might not be the sole factor in causing fatalities, long-term exposure demonstrated a positive correlation. This suggests that COVID-19 mortality is more strongly influenced by prolonged PM2.5 exposure rather than short-term exposure alone. Specifically, our regression analysis estimate that a 50 µg/m3 increase in long-term average PM2.5 could lead to an 11.9% rise in the COVID-19 mortality rate. Our research, conducted in one of the most polluted areas worldwide, offers compelling evidence regarding the influence of PM2.5 exposure on COVID-19 mortality rates. It emphasizes the importance of recognizing air pollution as a critical risk factor for the severity of viral respiratory infections.

Initially High Correlation between Air Pollution and COVID-19 Mortality Declined to Zero as the Pandemic Progressed: There Is No Evidence for a Causal Link between Air Pollution and COVID-19 Vulnerability.

Wu et al. found a strong positive association between cumulative daily county-level COVID-19 mortality and long-term average PM2.5 concentrations for data up until September 2020. We replicated the results of Wu et al. and extended the analysis up until May 2022. The association between PM2.5 concentration and cumulative COVID-19 mortality fell sharply after September 2020. Using the data available from Wu et al.’s “updated_data” branch up until May 2022, we found that the effect of a 1 μg/m3 increase in PM2.5 was associated with only a +0.603% mortality difference. The 95% CI of this difference was between −0.560% and +1.78%, narrow bounds that include zero, with the upper bound far below the Wu et al. estimate. Short-term trends in the initial spread of COVID-19, not a long-term epidemiologic association, caused an early correlation between air pollution and COVID-19 mortality.

Air quality around schools and school-level academic performance in Brazil

High levels of air pollution surrounding schools may be posing a risk to children's health, including cardiorespiratory diseases and cognitive deficits. The literature on the effects of air pollution outside schools on cognitive outcomes (e.g., cognitive development, working memory, and academic performance) is still limited. Most of the studies are from high-income countries. In this paper, we performed a nationwide assessment to estimate the association between air pollution (PM2.5 and NO2) and school-level academic performance in Brazil. We assessed academic performance data (high school national exam) from 25,390 high schools in Brazil. Air pollution data was derived from satellite remote sensing observations. Then we used mixed-effects regression models adjusted for several variables (e.g., school characteristics, spatio-temporal factors, and socioeconomic status) to estimate the association between air pollution (PM2.5 and NO2) and school-level academic performance. We found that an increase of 10 μg/m3 in the long-term average PM2.5 around the Brazilian schools is associated with 2.98 points lower (95%CI: 0.37; 5.58) in the school-level academic performance (considering that the performance score varies from 0 to 1000). For NO2, an increase of 10 ppb corresponds to 55.73 points lower (95%CI: 34.98; 76.48) in the school-level academic performance. This represents 0.05 and 1.02% lower points in academic performance, respectively for the exposure to PM2.5 and NO2.

Long-term nonlinear relationship between PM2.5 and ten leading causes of death.

Air pollution has become a major concern worldwide. Many epidemiological studies have proved relationships between fine particulate matter (PM2.5) and various diseases, but most studies only use short-term and models for specific groups to derive relationships with acute diseases. This makes it difficult to understand long-term exposure, nonlinear relationships, and spatial-temporal health risks regarding chronic diseases. Therefore, this study proposed to analyze and map PM2.5 exceedance probability from long-term spatial-temporal monitoring data using radial basis function estimation. We then constructed and compared multiple linear regression and generalized additive models to investigate linear and nonlinear relationships between long-term average PM2.5 concentration, PM2.5 potential probability for exceeding the standard, and standardized mortality for the top ten causes of death in all towns and villages in Taiwan nationally from 2010 to 2017. Linear models indicate that increasing PM2.5 concentration increased malignant neoplasm, pneumonia, and chronic lower respiratory disease mortalities; chronic liver diseases; and cirrhosis; whereas heart diseases and esophagus cancer mortality decreased. For the nonlinear model results, it can be found that there were also significant nonlinear relationships between PM2.5 concentration and malignant mortalities for neoplasm, heart disease, diabetes; and trachea, bronchus, lung, liver, intrahepatic bile duct, and esophagus cancer. Thus, long-term exposure to PM2.5 may be a significant risk factor for multiple acute and chronic diseases. Results from this study can be directly applied worldwide to provide air quality and health management references for governments, and important information on long-term health risks for local residents in the study area.

Chronic exposure to PM2.5 and COVID-19 mortality during the first year of the epidemic in Colombia: an ecological study

BACKGROUND AND AIM: The objective was to determine the association between chronic exposure to fine particulate matter (PM2.5) and COVID-19 mortality in Colombia. METHODS: Ecological study using data at the municipality level, as units of analysis. COVID-19 data were obtained from official reports up to and including March 6th, 2021. PM2.5 long-term exposure was defined as the 2014-2018 average of the estimated concentrations at municipalities obtained from the Copernicus Atmospheric Monitoring Service Reanalysis (CAMSRA) model. We fit a negative binomial model for the mortality rate adjusting for sociodemographic and health conditions. RESULTS:Estimated mortality rate ratios (MRR) for long-term average PM2.5 were not statistically significant in the model. We found that having 10% or more of the population over 65 years of age (MRR=1.16 95%CI 1.00-1.35), and the percentage of urban population (MRR=1.01 95%CI 1.01-1.01) are the main demographic factors associated with death rate at the municipality level. The poverty index and prevalence of chronic diseases, which were associated with COVID-19 mortality during the first months of the epidemic, are no longer statistically significant. CONCLUSIONS:There was no evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality rate at the municipality level in Colombia. Demographics did have evidence of an ecological effect on COVID-19 mortality. KEYWORDS: Air Pollution, Particulate Matter, Lon-term exposure, Mortality, Other (COVID-19)

Exploration of PM mass, source, and component-related factors that might explain heterogeneity in daily PM2.5-mortality associations across the United States

Multi-city epidemiologic studies examining short-term (daily) differences in fine particulate matter (PM2.5) provide evidence of substantial spatial heterogeneity in city-specific mortality risk estimates across the United States. Because PM2.5 is a mixture of particles, both directly emitted from sources or formed through atmospheric reactions, some of this heterogeneity may be due to regional variations in PM2.5 toxicity. Using inverse variance weighted linear regression, we examined change in percent change in mortality in association with 24 “exposure” determinants representing three basic groupings based on potential explanations for differences in PM toxicity – size, source, and composition. Percent changes in mortality for the PM2.5-mortality association for 313 core-based statistical areas and their metropolitan divisions over 1999–2005 were used as the outcome. Several determinants were identified as potential contributors to heterogeneity: all mass fraction determinants, vehicle miles traveled (VMT) for diesel total, VMT gas per capita, PM2.5 ammonium, PM2.5 nitrate, and PM2.5 sulfate. In multivariable models, only daily correlation of PM2.5 with PM10 and long-term average PM2.5 mass concentration were retained, explaining approximately 10% of total variability. The results of this analysis contribute to the growing body of literature specifically focusing on assessing the underlying basis of the observed spatial heterogeneity in PM2.5-mortality effect estimates, continuing to demonstrate that this heterogeneity is multifactorial and not attributable to a single aspect of PM.

Climate change, environmental factors, and COVID-19: Current evidence and urgent actions

Climate change, environmental factors, and COVID-19: Current evidence and urgent actions

Air pollution, sociodemographic and health conditions effects on COVID-19 mortality in Colombia: An ecological study

ObjectiveThe present study aimed to determine the association between chronic exposure to fine particulate matter (PM2.5), sociodemographic aspects, and health conditions with COVID-19 mortality in Colombia. MethodsWe performed an ecological study using data at the municipality level. We used COVID-19 data obtained from government public reports up to and including July 17th, 2020. We defined PM2.5 long-term exposure as the 2014–2018 average of the estimated concentrations at municipalities obtained from the Copernicus Atmospheric Monitoring Service Reanalysis (CAMSRA) model. We fitted a logit-negative binomial hurdle model for the mortality rate adjusting for sociodemographic and health conditions. ResultsEstimated mortality rate ratios (MRR) for long-term average PM2.5 were not statistically significant in either of the two components of the hurdle model (i.e., the likelihood of reporting at least one death or the count of fatal cases). We found that having 10% or more of the population over 65 years of age (MRR = 3.91 95%CI 2.24–6.81), the poverty index (MRR = 1.03 95%CI 1.01–1.05), and the prevalence of hypertension over 6% (MRR = 1.32 95%CI1.03–1.68) are the main factors associated with death rate at the municipality level. Having higher hospital beds capacity is inversely correlated to mortality. ConclusionsThere was no evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality rate at the municipality level in Colombia. Demographics, health system capacity, and social conditions did have evidence of an ecological effect on COVID-19 mortality. The use of model-based estimations of long-term PM2.5 exposure includes an undetermined level of uncertainty in the results, and therefore they should be interpreted as preliminary evidence.

Using a network of lower-cost monitors to identify the influence of modifiable factors driving spatial patterns in fine particulate matter concentrations in an urban environment.

There is substantial interest in using networks of lower-cost air quality sensors to characterize urban population exposure to fine particulate matter mass (PM2.5). However, sensor uncertainty is a concern with these monitors. (1) Quantify the uncertainty of lower-cost PM2.5 sensors; (2) Use the high spatiotemporal resolution of a lower-cost sensor network to quantify the contribution of different modifiable and non-modifiable factors to urban PM2.5. A network of 64 lower-cost monitors was deployed across Pittsburgh, PA, USA. Measurement and sampling uncertainties were quantified by comparison to local reference monitors. Data were sorted by land-use characteristics, time of day, and wind direction. Careful calibration, temporal averaging, and reference site corrections reduced sensor uncertainty to 1 μg/m3, ~10% of typical long-term average PM2.5 concentrations in Pittsburgh. Episodic and long-term enhancements to urban PM2.5 due to a nearby large metallurgical coke manufacturing facility were 1.6 ± 0.36 μg/m3 and 0.3 ± 0.2 μg/m3, respectively. Daytime land-use regression models identified restaurants as an important local contributor to urban PM2.5. PM2.5 above EPA and WHO daily health standards was observed at several sites across the city. With proper management, a large network of lower-cost sensors can identify statistically significant trends and factors in urban exposure.

Long-Term Effects of High Exposure to Ambient Fine Particulate Matter on Coronary Heart Disease Incidence: A Population-Based Chinese Cohort Study.

Evidence of long-term effects of high exposure to ambient fine particulate matter (PM2.5) on coronary heart disease (CHD) remains limited. We incorporated the high-resolution satellite-based PM2.5 estimates with a large-scale, population-based Chinese cohort comprising 118 229 individuals, to assess the CHD risk of long-term exposure to high PM2.5. During the follow-up of 908 376 person-years, 1586 incident CHD caseswere identified. The long-term average PM2.5 concentration for study population was 64.96 μg/m3, ranging from 31.17 to 96.96 μg/m3. For an increment of 10 μg/m3 in PM2.5, the multivariate-adjusted hazard ratios (HRs) were 1.43 (95% confidence interval [CI]: 1.35-1.51) for total CHD, 1.45 (95% CI: 1.36-1.56) for nonfatal CHD, and 1.38 (95% CI: 1.25-1.53) for fatal CHD, respectively. The effects were different across specific CHD outcomes, with greater effects for unstable angina (HR, 1.71 [95% CI, 1.56-1.88]), and weaker effects for acute myocardial infarction (HR, 1.28 [95% CI, 1.19-1.39]) and other CHD (HR, 1.27 [95% CI, 1.10-1.48]). The exposure-response curve suggested that HRs increased with elevated PM2.5 concentration over the entire exposure range. Elderly and hypertensive individuals were more susceptible to PM2.5-induced CHD. Our findings demonstrate the adverse health effects of severe air pollution and highlight the potential health benefits of air quality improvement.

Associations of long-term fine particulate matter exposure with prevalent hypertension and increased blood pressure in older Americans

BackgroundHypertension is a highly prevalent cardiovascular risk factor. It is possible that air pollution, also an established cardiovascular risk factor, may contribute to cardiovascular disease through increasing blood pressure. Previous studies evaluating associations between air pollution and blood pressure have had mixed results. MethodsWe examined the association between long-term (one-year moving average) air pollutant exposures, prevalent hypertension and blood pressure in 4121 older Americans (57+ years) enrolled in the National Social Life, Health, and Aging Project. We estimated exposures to PM2.5 using spatio-temporal models and used logistic regression accounting for repeated measures to evaluate the association between long-term average PM2.5 and prevalence odds of hypertension. We additionally used linear regression to evaluate the associations between air pollutants and systolic, diastolic, mean arterial, and pulse pressures. Health effect models were adjusted for a number of demographic, health and socioeconomic covariates. ResultsAn inter-quartile range (3.91 μg/m3) increase in the one-year moving average of PM2.5 was associated with increased: Odds of prevalent hypertension (POR 1.24, 95% CI: 1.11, 1.38), systolic blood pressure (0.93 mm Hg, 95% CI: 0.05, 1.80) and pulse pressure (0.89 mm Hg, 95% CI: 0.21, 1.58). Dose-response relationships were also observed. ConclusionsPM2.5 was associated with increased odds of prevalent hypertension, and increased systolic pressure and pulse pressure in a cohort of older Americans. These findings add to the growing evidence that air pollution may be an important risk factor for hypertension and perturbations in blood pressure.

New Insights into PM2.5 and Life-Expectancy Association using Predicted County-Average PM2.5 Concentrations

Background/Aim: Much of the current evidence of associations between long-term average PM2.5 exposures and health outcomes relies on exposures derived directly from regulatory monitoring data, using all available monitors in an area. These studies could be affected by limited data, particularly for time periods before 1999 when spatially extensive monitoring began. For instance, Pope et al. (2009) showed that between 1980 and 2000 a 10 µg/m3 reduction in PM2.5 was associated with 0.61±0.20 year longer life expectancy. That analysis used 1979-1983 averages of PM2.5 across 51 U.S. metropolitan statistical areas estimated from about 100 monitoring sites in the Inhalable Particulate Network. Our reanalysis re-examines this association using population-representative county-level predicted concentrations of PM2.5. Methods: We used the same life expectancy data in 211 continental U.S. counties as Pope et al. (2009). We predicted annual average PM2.5 concentrations at 70,000 census tract centroids for 1979-2004, using a previously developed point prediction model. We averaged these to the county level for the same years (1979-1983 and 1999-2000) and estimated regression coefficients for a PM2.5 reduction on life expectancy improvement over the 1980 and 2000 time periods, adjusting for area-level confounders. Results: We found a 10 µg/m3 decrease in predicted county average PM2.5 was associated with an average 0.69±0.31 year increase in life expectancy, slightly higher than the estimate reported by Pope et al. (2009). The PM2.5 effect estimates varied by pollutant averaging period and were larger when more recent years were included. However, all effect estimates had overlapping confidence intervals. Conclusions: Our approach to estimating population-representative PM2.5 concentrations lends additional support to the evidence that reduced in fine particulate matter contributes to improved life expectancy. Our estimates had wider confidence intervals because our county-averaged concentrations are less variable than those estimated directly from limited monitoring data.

PM2.5 exposure and incident diabetes and hypertension: Preliminary results from the Black Women’s Health Study

Background. Laboratory evidence suggests that fine particulate matter PM2.5 may contribute to the genesis of chronic conditions like diabetes and hypertension. Although air pollution has been associated with the prevalence of these conditions, the few epidemiologic data on its relation to incident diabetes and hypertension have been inconsistent. Aims. We assessed the association of ambient PM2.5 and incident type 2 diabetes and hypertension in the Black Women’s Health Study (BWHS), a follow-up study of African American women. Methods. BWHS participants reported incident diabetes and hypertension on biennial questionnaires from 1995-2011. We estimated long-term average PM2.5 levels at participant addresses from 1998-2009 in 56 cities across the U.S. using a hybrid model combining land use regression and Bayesian Maximum Entropy kriging. We used Cox regression models to estimate city-specific hazard ratios (HR) associated with a 1.75 µg/m3 increase in PM2.5, the mean interquartile range in the major cities. City-specific HRs were then pooled in a summary estimate. Results. Of 33,621 women free of diabetes in 1995, 4078 developed diabetes over 16 years of follow-up; of 32,638 hypertension-free women in 1995, 9588 developed hypertension. The age-adjusted HR for diabetes was 1.09 (95% CI 1.03-1.15). After control for age, body mass index, and neighborhood socioeconomic status, it was 1.02 (95% CI 0.96-1.08). For hypertension, the respective HRs were 1.03 (95% CI 1.00-1.07) and 0.98 (95% CI 0.95-1.02). Conclusions. These results do not provide evidence of an association between PM2.5 and incident diabetes or hypertension in black women. Future work will assess the effects of traffic-related air pollutants, which were previously associated with incident diabetes in BWHS participants living in Los Angeles (Coogan et al., Circulation 2012;125:767-772).

Assessing Long-Term Exposure in the California Teachers Study

Assessing Long-Term Exposure in the California Teachers Study

Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application

BackgroundEpidemiologic and health impact studies of fine particulate matter with diameter < 2.5 μm (PM2.5) are limited by the lack of monitoring data, especially in developing countries. Satellite observations offer valuable global information about PM2.5 concentrations.ObjectiveIn this study, we developed a technique for estimating surface PM2.5 concentrations from satellite observations.MethodsWe mapped global ground-level PM2.5 concentrations using total column aerosol optical depth (AOD) from the MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite instruments and coincident aerosol vertical profiles from the GEOS-Chem global chemical transport model.ResultsWe determined that global estimates of long-term average (1 January 2001 to 31 December 2006) PM2.5 concentrations at approximately 10 km × 10 km resolution indicate a global population-weighted geometric mean PM2.5 concentration of 20 μg/m3. The World Health Organization Air Quality PM2.5 Interim Target-1 (35 μg/m3 annual average) is exceeded over central and eastern Asia for 38% and for 50% of the population, respectively. Annual mean PM2.5 concentrations exceed 80 μg/m3 over eastern China. Our evaluation of the satellite-derived estimate with ground-based in situ measurements indicates significant spatial agreement with North American measurements (r = 0.77; slope = 1.07; n = 1057) and with noncoincident measurements elsewhere (r = 0.83; slope = 0.86; n = 244). The 1 SD of uncertainty in the satellite-derived PM2.5 is 25%, which is inferred from the AOD retrieval and from aerosol vertical profile errors and sampling. The global population-weighted mean uncertainty is 6.7 μg/m3.ConclusionsSatellite-derived total-column AOD, when combined with a chemical transport model, provides estimates of global long-term average PM2.5 concentrations.

AOD–PM 2.5 Association: Paciorek and Liu Respond

AOD–PM _2.5 Association: Paciorek and Liu Respond

Outdoor air pollution and lung cancer: recent epidemiologic evidence.

Outdoor air pollution and lung cancer: recent epidemiologic evidence.