Wildland Fires Worsened Population Exposure to Pm

As wildland fires become more frequent and intense, fire smoke has significantly worsened the ambient air quality, posing greater health risks. to better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily P

Estimating PM\u3csub\u3e2.5\u3c/sub\u3ein Southern California using satellite data: Factors that affect model performance

Background: Studies of PM2.5 health effects are influenced by the spatiotemporal coverage and accuracy of exposure estimates. The use of satellite remote sensing data such as aerosol optical depth (AOD) in PM2.5 exposure modeling has increased recently in the US and elsewhere in the world. However, few studies have addressed this issue in southern California due to challenges with reflective surfaces and complex terrain. Methods: We examined the factors affecting the associations with satellite AOD using a two-stage spatial statistical model. The first stage estimated the temporal PM2.5/AOD relationships using a linear mixed effects model at 1 km resolution. The second stage accounted for spatial variation using geographically weighted regression. Goodness of fit for the final model was evaluated by comparing the daily PM2.5 concentrations generated by cross-validation (CV) with observations. These methods were applied to a region of southern California spanning from Los Angeles to San Diego. Results: Mean predicted PM2.5 concentration for the study domain was 8.84 µg m-3. Linear regression between CV predicted PM2.5 concentrations and observations had an R 2 of 0.80 and RMSE 2.25 µg m-3. The ratio of PM2.5 to PM10 proved an important variable in modifying the AOD/PM2.5 relationship (β = 14.79, p ≤ 0.001). Including this ratio improved model performance significantly (a 0.10 increase in CV R 2 and a 0.56 µg m-3 decrease in CV RMSE). Discussion: Utilizing the high-resolution MAIAC AOD, fine-resolution PM2.5 concentrations can be estimated where measurements are sparse. This study adds to the current literature using remote sensing data to achieve better exposure data in the understudied region of Southern California. Overall, we demonstrate the usefulness of MAIAC AOD and the importance of considering coarser particles in dust prone areas

Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA

Ambient exposure to fine particulate matter (PM2.5) is one of the top global health concerns. We estimate the PM2.5-related health benefits of emission reduction over New York State (NYS) from 2002 to 2012 using seven publicly available PM2.5 products that include information from ground-based observations, remote sensing and chemical transport models. While these PM2.5 products differ in spatial patterns, they show consistent decreases in PM2.5 by 28%–37% from 2002 to 2012. We evaluate these products using two sets of independent ground-based observations from the New York City Community Air Quality Survey (NYCCAS) Program for an urban area, and the Saint Regis Mohawk Tribe Air Quality Program for a remote area. Inclusion of satellite remote sensing improves the representativeness of surface PM2.5 in the remote area. Of the satellite-based products, only the statistical land use regression approach captures some of the spatial variability across New York City measured by NYCCAS. We estimate the PM2.5-related mortality burden by applying an integrated exposure-response function to the different PM2.5 products. The multi-product mean PM2.5-related mortality burden over NYS decreased by 5660 deaths (67%) from 8410 (95% confidence interval (CI): 4570–12 400) deaths in 2002 to 2750 (CI: 700–5790) deaths in 2012. We estimate a 28% uncertainty in the state-level PM2.5 mortality burden due to the choice of PM2.5 products, but such uncertainty is much smaller than the uncertainty (130%) associated with the exposure-response function

Evaluating the utility of high-resolution spatiotemporal air pollution data in estimating local PM2.5 exposures in California from 2015-2018

Air quality management is increasingly focused not only on across-the-board reductions in ambient pollution concentrations but also on identifying and remediating elevated exposures that often occur in traditionally disadvantaged communities. Remote sensing of ambient air pollution using data derived from satellites has the potential to better inform management decisions that address environmental disparities by providing increased spatial coverage, at high-spatial resolutions, compared to air pollution exposure estimates based on ground-based monitors alone. Daily PM2.5 estimates for 2015–2018 were estimated at a 1 km2 resolution, derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in order to assess the utility of highly refined spatiotemporal air pollution data in 92 California cities and in the 13 communities included in the California Community Air Protection Program. The identification of pollution hot-spots within a city is typically not possible relying solely on the regulatory monitoring networks; however, day-to-day temporal variability was shown to be generally well represented by nearby ground-based monitoring data even in communities with strong spatial gradients in pollutant concentrations. An assessment of within-ZIP Code variability in pollution estimates indicates that high-resolution pollution estimates (i.e., 1 km2) are not always needed to identify spatial differences in exposure but become increasingly important for larger geographic areas (approximately 50 km2). Taken together, these findings can help inform strategies for use of remote sensing data for air quality management including the screening of locations with air pollution exposures that are not well represented by existing ground-based air pollution monitors.11Nsciescopu

Improved spatial representation of a highly resolved emission inventory in China: evidence from TROPOMI measurements

Emissions in many sources are estimated in municipal district totals and spatially disaggregated onto grid cells using empirically selected spatial proxies such as population density, which might introduce biases, especially in fine spatial scale. Efforts have been made to improve the spatial representation of emission inventory, by incorporating comprehensive point source database (e.g. power plants, industrial facilities) in emission estimates. Satellite-based observations from the TROPOspheric Monitoring Instrument (TROPOMI) with unprecedented pixel sizes (3.5 × 7 km ^2 ) and signal-to-noise ratios offer the opportunity to evaluate the spatial accuracy of such highly resolved emissions from space. Here, we compare the city-level NO _x emissions from a proxy-based emission inventory named the Multi-resolution Emission Inventory for China (MEIC) with a highly resolved emission inventory named the Multi-resolution Emission Inventory for China - High Resolution (MEIC-HR) that has nearly 100 000 industrial facilities, and evaluate them through NO _x emissions derived from the TROPOMI NO _2 tropospheric vertical column densities (TVCDs). We find that the discrepancies in city-level NO _x emissions between MEIC and MEIC-HR are influenced by the proportions of emissions from point sources and NO _x emissions per industrial gross domestic product (IGDP). The use of IGDP as a spatial proxy to disaggregate industrial emissions tends to overestimate NO _x emissions in cities with lower industrial emission intensities or less industrial facilities in the MEIC. The NO _x emissions of 70 cities are derived from one year TROPOMI NO _2 TVCDs using the exponentially modified Gaussian function. Compared to the satellite-derived emissions, the cities with higher industrial point source emission proportions in MEIC-HR agree better with space-constrained results, indicating that integrating more point sources in the inventory would improve the spatial accuracy of emissions on city scale. In the future, we should devote more efforts to incorporating accurate locations of emitting facilities to reduce uncertainties in fine-scale emission estimates and guide future policies

A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa

Please read abstract in the article.https://www.elsevier.com/locate/rse2023-09-23hj2022Geography, Geoinformatics and Meteorolog

Detection of Solar-like Oscillations in Subgiant and Red Giant Stars Using 2 minute Cadence TESS Data

Based on all 2 minute cadence TESS light curves from Sector 1 to 60, we provide a catalog of 8651 solar-like oscillators, including frequency at maximum power ( ${\nu }_{\max }$ , with its median precision σ = 5.39%), large frequency separation (Δ ν , σ = 6.22%), and seismically derived masses, radii, and surface gravity values. In this sample, we have detected 2173 new oscillators and added 4373 new Δ ν measurements. Our seismic parameters are consistent with those from Kepler, K2, and previous TESS data. The median fractional residual in ${\nu }_{\max }$ is 1.63%, with a scatter of 14.75%, and in Δ ν it is 0.11%, with a scatter of 10.76%. We have detected 476 solar-like oscillators with ${\nu }_{\max }$ exceeding the Nyquist frequency of Kepler long-cadence data during the evolutionary phases of subgiants and the base of the red giant branch, which provide a valuable resource for understanding angular momentum transport

Developing an Advanced PM2.5 Exposure Model in Lima, Peru

It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima’s topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was −0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies

Estimating PM2.5 in Southern California using satellite data: factors that affect model performance

Background:Studies of PM(2.5)health effects are influenced by the spatiotemporal coverage and accuracy of exposure estimates. The use of satellite remote sensing data such as aerosol optical depth (AOD) in PM(2.5)exposure modeling has increased recently in the US and elsewhere in the world. However, few studies have addressed this issue in southern California due to challenges with reflective surfaces and complex terrain. Methods:We examined the factors affecting the associations with satellite AOD using a two-stage spatial statistical model. The first stage estimated the temporal PM2.5/AOD relationships using a linear mixed effects model at 1 km resolution. The second stage accounted for spatial variation using geographically weighted regression. Goodness of fit for the final model was evaluated by comparing the daily PM(2.5)concentrations generated by cross-validation (CV) with observations. These methods were applied to a region of southern California spanning from Los Angeles to San Diego. Results:Mean predicted PM(2.5)concentration for the study domain was 8.84 mu g m(-3). Linear regression between CV predicted PM(2.5)concentrations and observations had anR(2)of 0.80 and RMSE 2.25 mu g m(-3). The ratio of PM(2.5)to PM(10)proved an important variable in modifying the AOD/PM(2.5)relationship (beta = 14.79, p <= 0.001). Including this ratio improved model performance significantly (a 0.10 increase in CVR(2)and a 0.56 mu g m(-3)decrease in CV RMSE). Discussion:Utilizing the high-resolution MAIAC AOD, fine-resolution PM(2.5)concentrations can be estimated where measurements are sparse. This study adds to the current literature using remote sensing data to achieve better exposure data in the understudied region of Southern California. Overall, we demonstrate the usefulness of MAIAC AOD and the importance of considering coarser particles in dust prone areas.11Nsciescopu

Investigating 16 Open Clusters in the Kepler/K2-Gaia DR3 field. I. Membership, Binary, and Rotation

Using data from the Gaia Data Release 3 (Gaia DR3) and Kepler/K2, we present a catalog of 16 open clusters with ages ranging from 4 to 4000 Myr, which provides detailed information on membership, binary systems, and rotation. We assess the memberships in 5D phase space, and estimate the basic parameters of each cluster. Among the 20,160 members, there are 4,381 stars identified as binary candidates and 49 stars as blue straggler stars. The fraction of binaries vary in each cluster, and the range between 9% to 44%. We obtain the rotation periods of 5,467 members, of which 4,304 are determined in this work. To establish a benchmark for the rotation-age-color relation, we construct color-period diagrams. We find that the rotational features of binaries are similar to that of single stars, while features for binaries are more scattered in the rotation period. Moreover, the morphology of the color-period relationship is already established for Upper Scorpius at the age of 19 Myr, and some stars of varying spectral types (i.e. FG-, K-, and M-type) show different spin-down rates after the age of ~110 Myr. By incorporating the effects of stalled spin-down into our analysis, we develop an empirical rotation-age-color relation, which is valid with ages between 700 - 4000 Myr and colors corresponding to a range of 0.5 < (G_BP-G_RP)0 < 2.5 mag.Comment: 21 pages, 17 figures, accepted for publication in ApJ