Published: Oct. 29, 2024
Language: Английский
Published: Oct. 29, 2024
Language: Английский
Ecological Indicators, Journal Year: 2025, Volume and Issue: 175, P. 113509 - 113509
Published: May 17, 2025
Language: Английский
Citations
0JAMA, Journal Year: 2024, Volume and Issue: 332(12), P. 1011 - 1011
Published: July 10, 2024
This JAMA Insights explores the adverse effects of wildfires on human health and care systems offers suggestions how clinicians can help mitigate threats posed by wildfires.
Language: Английский
Citations
3EarthArXiv (California Digital Library), Journal Year: 2024, Volume and Issue: unknown
Published: June 13, 2024
Growing wildfire smoke represents a substantial threat to air quality and human health in the US across much of globe. However, impact on remains imprecisely understood, due uncertainties both measurement population exposure dose-response functions linking health. Here, we compare daily smoke-related surface fine particulate matter (PM2.5) concentrations estimated using three approaches, including two chemical transport models (CTMs): GEOS-Chem Community Multiscale Air Quality (CMAQ), one machine learning (ML) model over contiguous 2020, historically active fire year. We study consequences these different approaches for estimating PM2.5 effects mortality. In western US, compared against measurements from Environmental Protection Agency (EPA) PurpleAir sensors, find that CTMs overestimate during extreme episodes by up 3-5 fold, while ML estimates are largely consistent with measurements. eastern where levels were lower show modestly better agreement develop calibration framework integrates CTM- ML-based yields outperform each individual approach. When combining county-level mortality rates, low-level but large discrepancies high-level methods. Our research highlights benefits costs estimation methods understanding impacts smoke, demonstrates importance bench-marking available
Language: Английский
Citations
2ISPRS International Journal of Geo-Information, Journal Year: 2024, Volume and Issue: 13(7), P. 217 - 217
Published: June 22, 2024
Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many models ignore spatial non-stationarity predictive variables. To address this issue, study introduces Flexible Geographically Weighted Neural Network (FGWNN) estimate PM2.5 based on multi-source data. FGWNN incorporates Geographical Neuron (FGN) and Activation Function (GWAF) within framework Artificial (ANN) capture intricate non-stationary relationships among A robust air estimation model was constructed using data Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), Terrain Elevation (HGT) as inputs, Ground-Based observation. The results indicated that successfully generates with 2.5 km resolution for contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared traditional ANN Regression (GWR) models. holds potential applications high-precision high-resolution scenarios.
Language: Английский
Citations
2Environment International, Journal Year: 2024, Volume and Issue: 193, P. 109101 - 109101
Published: Oct. 28, 2024
Language: Английский
Citations
1Environmental Science & Technology, Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 18, 2024
Growing wildfire smoke represents a substantial threat to air quality and human health. However, the impact of on health remains imprecisely understood due uncertainties in both measurement exposure population dose-response functions linking Here, we compare daily smoke-related surface fine particulate matter (PM
Language: Английский
Citations
1Published: Jan. 1, 2024
Language: Английский
Citations
0International Journal of Environmental Science and Technology, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 8, 2024
Abstract
This
study
investigates
the
seasonal
influence
on
nitrogen
oxide
NO
x
pollution
records
at
four
monitoring
sites
in
Mexico
City
from
2010
to
2018.
The
analysis
employs
second-order
structure
function
examine
trends
concentration
fluctuations.
findings
reveal
that
fluctuations
follow
a
power
law
pattern
characterized
by
Hurst
exponents,
predominantly
statistical
persistence
regime,
with
scaling
range
spanning
three
orders
of
magnitude.
Specifically,
autumn
period
exhibits
an
exponent
$$\overline{H}=0.72$$
Language: Английский
Citations
0Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 373, P. 123612 - 123612
Published: Dec. 5, 2024
Language: Английский
Citations
0Published: Oct. 29, 2024
Language: Английский
Citations
0