Ambient fine particulate matter (PM2.5) is the world’s leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5exposure. Here we interpret satellite-derived PM2.5estimates over 1998-2019 and find a reversal of previous growth in global PM2.5air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m3) to a peak in 2011 (38.9 μg/m3) and decreased steadily afterwards (34.7 μg/m3in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3marginal reduction in exposure, implying increasing urgency and benefits of PM2.5mitigation with aging population and cleaner air.
Despite improvements in ambient air quality in the US in recent decades, many people still experience unhealthy levels of pollution. At present, national‐level alert‐day identification relies predominately on surface monitor networks and forecasters. Satellite‐based estimates of surface air quality have rapidly advanced and have the capability to inform exposure‐reducing actions to protect public health. At present, we lack a robust framework to quantify public health benefits of these advances in applications of satellite‐based atmospheric composition data. Here, we assess possible health benefits of using geostationary satellite data, over polar orbiting satellite data, for identifying particulate air quality alert days (24hr PM2.5 > 35 μg m−3) in 2020. We find the more extensive spatiotemporal coverage of geostationary satellite data leads to a 60% increase in identification of person‐alerts (alert days × population) in 2020 over polar‐orbiting satellite data. We apply pre‐existing estimates of PM2.5exposure reduction by individual behavior modification and find these additional person‐alerts may lead to 1,200 (800–1,500) or 54% more averted PM2.5‐attributable premature deaths per year, if geostationary, instead of polar orbiting, satellite data alone are used to identify alert days. These health benefits have an associated economic value of 13 (8.8–17) billion dollars ($2019) per year. Our results highlight one of many potential applications of atmospheric composition data from geostationary satellites for improving public health. Identifying these applications has important implications for guiding use of current satellite data and planning future geostationary satellite missions.
more » « less- Award ID(s):
- 2019494
- PAR ID:
- 10553351
- Publisher / Repository:
- GeoHealth
- Date Published:
- Journal Name:
- GeoHealth
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2471-1403
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Previous research on the health and air quality impacts of wildfire smoke has largely focused on the impact of smoke on outdoor air quality; however, many people spend a majority of their time indoors. The quality of indoor air on smoke-impacted days is largely unknown. In this analysis, we use publicly available data from an existing large network of low-cost indoor and outdoor fine particulate matter (PM2.5) monitors to quantify the relationship between indoor and outdoor particulate air quality on smoke-impacted days in 2020 across the western United States (US). We also investigate possible regional and socioeconomic trends in this relationship for regions surrounding six major cities in the western US. We find indoor PM2.5concentrations are 82% or 4.2
µ g m−3(median across all western US indoor monitors for the year 2020; interquartile range, IQR: 2.0–7.2µ g m−3) higher on smoke-impacted days compared to smoke-free days. Indoor/outdoor PM2.5ratios show variability by region, particularly on smoke-free days. However, we find the ratio of indoor/outdoor PM2.5is less than 1 (i.e. indoor concentrations lower than outdoor) at nearly all indoor-outdoor monitor pairs on smoke-impacted days. Although typically lower than outdoor concentrations on smoke-impacted days, we find that on heavily smoke-impacted days (outdoor PM2.5> 55µ g m−3), indoor PM2.5concentrations can exceed the 35µ g m−324 h outdoor standard set by the US Environmental Protection Agency. Further, total daily-mean indoor PM2.5concentrations increase by 2.1µ g m−3with every 10µ g m−3increase in daily-mean outdoor PM2.5.(median of statistically significant linear regression slopes across all western US monitor pairs; IQR: 1.0–4.3µ g m−3) on smoke-impacted days. These results show that for indoor environments in the western US included in our analysis, remaining indoors during smoke events is currently an effective, but limited, strategy to reduce PM2.5exposure. -
Wildfires and meteorological conditions influence the co-occurrence of multiple harmful air pollutants including fine particulate matter (PM 2.5 ) and ground-level ozone. We examine the spatiotemporal characteristics of PM 2.5 /ozone co-occurrences and associated population exposure in the western United States (US). The frequency, spatial extent, and temporal persistence of extreme PM 2.5 /ozone co-occurrences have increased significantly between 2001 and 2020, increasing annual population exposure to multiple harmful air pollutants by ~25 million person-days/year. Using a clustering methodology to characterize daily weather patterns, we identify significant increases in atmospheric ridging patterns conducive to widespread PM 2.5 /ozone co-occurrences and population exposure. We further link the spatial extent of co-occurrence to the extent of extreme heat and wildfires. Our results suggest an increasing potential for co-occurring air pollution episodes in the western US with continued climate change.more » « less
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Abstract In an era where air pollution poses a significant threat to both the environment and public health, we present a network-based approach to unravel the dynamics of extreme pollution events. Leveraging data from 741 monitoring stations in the contiguous United States, we have created dynamic networks using time-lagged correlations of hourly particulate matter (PM2.5) data. The established spatial correlation networks reveal significant PM2.5anomalies during the 2020 and 2021 wildfire seasons, demonstrating the approach’s sensitivity to detecting regional pollution phenomena. The methodology also provides insights into smoke transport and network response, highlighting the persistence of air quality issues beyond visible smoke periods. Additionally, we explored meteorological variables’ impacts on network connectivity. This study enhances understanding of spatiotemporal pollution patterns, positioning spatial correlation networks as valuable tools for environmental monitoring and public health surveillance.
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Introduction: Traditional methods to estimate exposure to PM2.5(particulate matter with less than 2.5 µm in diameter) have typically relied on limited regulatory monitors and do not consider human mobility and travel. However, the limited spatial coverage of regulatory monitors and the lack of consideration of mobility limit the ability to capture actual air pollution exposure.Methods: This study aims to improve traditional exposure assessment methods for PM2.5by incorporating the measurements from a low-cost sensor network (PurpleAir) and regulatory monitors, an automated machine learning modeling framework, and big human mobility data. We develop a monthly-aggregated hourly land use regression (LUR) model based on automated machine learning (AutoML) and assess the model performance across eight metropolitan areas within the US.Results: Our results show that integrating low-cost sensor with regulatory monitor measurements generally improves the AutoML-LUR model accuracy and produces higher spatial variation in PM2.5concentration maps compared to using regulatory monitor measurements alone. Feature importance analysis shows factors highly correlated with PM2.5concentrations, including satellite aerosol optical depth, meteorological variables, vegetation, and land use. In addition, we incorporate human mobility data on exposure estimates regarding where people visit to identify spatiotemporal hotspots of places with higher risks of exposure, emphasizing the need to consider both visitor numbers and PM2.5concentrations when developing exposure reduction strategies.Discussion: This research provides important insights for further public health studies on air pollution by comprehensively assessing the performance of AutoML-LUR models and incorporating human mobility into considering human exposure to air pollution.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1029/2023GH000890
