Hazard Mapping System Research Articles

Exploring relationships between smoke exposure, housing characteristics, and preterm birth in California

Pregnant people are vulnerable to air pollution exposure, including risk of preterm birth, low birth weight, and stillbirth. Understanding the infiltration of outdoor wildfire smoke into a residential space is critical for the accurate assessment of wildfire smoke exposure and associated health effects in pregnant people. Relying on ambient measurements of wildfire smoke alone can result in exposure misclassification. In this study, we examine the role of physical housing characteristics in the relationship between smoke exposure and preterm birth. In particular, we examine the effect of home size, year of construction, cooling type, and renovation status, as effect modifiers in the relationship between smoke exposure during pregnancy and preterm birth from 2007 to 2015 in California. To do this, we combined data on home characteristics from the California Tax Assessor, birth outcomes from the California birth records database, and the number of smoke days for each pregnancy from the NOAA (National Oceanic and Atmospheric Administration) Hazard Mapping System (HMS). We estimated the association between smoke day exposures and odds of preterm birth using logistic regression models and stratified by air basin and housing characteristics. Our findings reveal that cooling type and renovation status are key factors modifying the smoke exposure-preterm birth relationship. Notably, we found elevated associations for people living in unrenovated homes, those using evaporative cooling systems, and those using central air conditioning units. While we observed elevated odds of preterm birth associated with increasing smoke day exposure for residents of large and new homes, this effect does not significantly differ across home size and age quartiles. This study highlights the need to further examine the relative roles of housing characteristics as well as factors not measured here including behavioral factors, time spent outdoors, window use, and occupational exposures in driving adverse birth outcomes related to wildfire smoke exposure.

Gestational and postnatal exposure to wildfire smoke and prolonged use of respiratory medications in early life

As wildfire frequency and severity increases, smoke exposures will cause increasingly more adverse respiratory effects. While acute respiratory effects of smoke exposure have been documented in children, longer term sequelae are largely unstudied. Our objective here was to examine the association between gestational and postnatal exposure to wildfire smoke and prolonged use of prescription medication for respiratory conditions in early childhood. Using Merative MarketScan claims data, we created cohorts of term children born in western states between 1 January 2010-31 December 2014 followed for at least three years. Using NOAA Hazard Mapping System data, we determined the average number of days a week that >25% of the population in a metropolitan statistical area (MSA) was covered by smoke within each exposure period. The exposure periods were defined by trimester and two 12 week postnatal periods. Medication use was based on respiratory indication (upper respiratory, lower respiratory, or any respiratory condition) and categorized into outcomes of prolonged use (⩾30 d use) (PU) and multiple prolonged uses (at least two prolonged uses) (MPU). We used logistic regression models with random intercepts for MSAs adjusted for child sex, birth season, and birth year. Associations differed by exposure period and respiratory outcome, with elevated risk of MPU of lower respiratory medications following exposure in the third trimester and the first 12 postnatal weeks (RR 1.15, 95% CI 0.98, 1.35; RR 1.21, 95% CI 1.05, 1.40, respectively). Exposure in the third trimester was associated with an increase in MPU of any respiratory among males infants only (male RR 1.22, 95% CI 1.00, 1.50; female RR 0.93, 95% CI 0.66, 1.31). Through novel use of prescription claims data, this work identifies critical developmental windows in the 3rd trimester and first 12 postnatal weeks during which environmental inhalational disaster events may impact longer-term respiratory health.

Wildfire Impacts on O3 in the Continental United States Using PM2.5 and a Generalized Additive Model (2018-2023).

We examined PM2.5 and Hazard Mapping System smoke plume satellite data at ∼600 United States (US) air monitoring stations to identify surface smoke on 14.0% of all May-September days for 2018-2023, with large influences in 2020 and 2021, due to California fires, and 2023, due to Canadian fires. Days with smoke have an average of 11 μg m-3 more PM2.5 and 8 ppb higher maximum daily 8 h average (MDA8) O3 concentrations than nonsmoke days, and they also account for 94% of all days that exceed the daily PM2.5 health standard (35 μg m-3) and 36% of all days that exceed the O3 health standard (70 ppb). To estimate the smoke contributions to the O3 MDA8, Generalized Additive Models (GAMs) were built for each site using the nonsmoke day data and up to 8 predictors. The mean and standard deviation of the residuals from the GAMs were 0 ± 6.1 ppb for the nonsmoke day data and 4.3 ± 7.9 ppb for the smoke day data, indicating a significant enhancement in the MDA8 O3 on smoke days. We found positive residuals on 72% of the smoke days and for these days, we calculate an average smoke contribution to the O3 MDA8 of 7.8 ± 6.0 ppb. Over the 6 year period, the percentage of exceedance days due to smoke in the continental US was 25% of all exceedance days, and the highest was in 2023 (38%). In 2023, the Central US experienced an unusually high number of exceedance days, 1522, with 52% of these impacted by smoke, while the Eastern US had fewer exceedance days, 288, with 78% of these impacted by smoke. Our results demonstrate the importance of wildland fires as contributors to exceedances of the health-based national air quality standards for PM2.5 and O3.

The Camp fire and perinatal health: An example of the generalized synthetic control method to identify susceptible windows of exposure.

The November 2018 Camp fire was the most destructive wildfire in California history, but its effects on reproductive health are not known. We linked California birth records from 2017-2019 to daily smoke levels using U.S. EPA Air Quality System (AQS) PM2.5 data and NOAA Hazard Mapping System smoke plume polygons during the Camp fire. In the main analysis, pregnancies were considered exposed if they had median AQS PM2.5 levels above 50 μg/m3 for at least 7 days during November 8-22, 2018. We calculated rates of preterm birth and the infant sex ratio based on week of conception and used the generalized synthetic control method to estimate the average treatment effect on the treated and to propose a novel approach to identify potential critical weeks of exposure during pregnancy. We found associations between Camp fire-related smoke exposure and rates of preterm birth, with a risk difference (RD) = 0.005, 95% CI 0.001, 0.010. Exposure during week 10 of pregnancy was consistently associated with increased preterm birth (RD = 0.030, 95% CI 0.004, 0.056). We did not observe differences in the infant sex ratio. Camp fire smoke exposure was associated with increased rates of preterm birth, with sensitive windows in the first trimester.

Daily Fine Resolution Estimates of the Influence of Wildfires on Fine Particulate Matter in California, 2011–2020

Worsening wildfire seasons in recent years are reversing decadal progress on the reduction of harmful air pollutants in the US, particularly in Western states. Measurements of the contributions of wildfire smoke to ambient air pollutants, such as fine particulate matter (PM2.5), at fine resolution scales would be valuable to public health research on climate vulnerable populations and compound climate risks. We estimate the influence of wildfire smoke emissions on daily PM2.5 at fine-resolution, 3 km, for California 2011–2020, using a geostatistical modeled ambient PM2.5 estimate and wildfire smoke plume data from NOAA Hazard Mapping System. Additionally, we compare this product with the US Environmental Protection Agency (EPA) daily and annual standards for PM2.5 exposure. Our results show wildfires significantly influence PM2.5 in California and nearly all exceedances of the daily US EPA PM2.5 standard were influenced by wildfire smoke, while annual exceedances were increasingly attributed to wildfire smoke influence in recent years. This wildfire-influenced PM2.5 product can be applied to public health research to better understand source-specific air pollution impacts and assess the combination of multiple climate hazard risks.

Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = −0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

PM2.5 Is Insufficient to Explain Personal PAH Exposure.

To understand how chemical exposure can impact health, researchers need tools that capture the complexities of personal chemical exposure. In practice, fine particulate matter (PM2.5) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure, but do not capture total chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. In this study, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a 7-day period in multiple seasons. We gathered publicly available daily PM2.5 AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons. Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM2.5 AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM2.5 AQI only, HMS only, and a multivariate feature set including PM2.5 AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM2.5 AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.

Is the smoke aloft? Caveats regarding the use of the Hazard Mapping System (HMS) smoke product as a proxy for surface smoke presence across the United States

Is the smoke aloft? Caveats regarding the use of the Hazard Mapping System (HMS) smoke product as a proxy for surface smoke presence across the United States

Background nitrogen dioxide (NO2) over the United States and its implications for satellite observations and trends: effects of nitrate photolysis, aircraft, and open fires

Abstract. Tropospheric nitrogen dioxide (NO2) measured from satellites has been widely used to track anthropogenic NOx emissions, but its retrieval and interpretation can be complicated by the free tropospheric NO2 background to which satellite measurements are particularly sensitive. Tropospheric NO2 vertical column densities (VCDs) from the spaceborne Ozone Monitoring Instrument (OMI) averaged over the contiguous US (CONUS) show no trend after 2009, despite sustained decreases in anthropogenic NOx emissions, implying an important and rising contribution from the free tropospheric background. Here, we use the GEOS-Chem chemical transport model applied to the simulation of OMI NO2 to better understand the sources and trends of background NO2 over CONUS. The previous model underestimate of the background is largely corrected by the consideration of aerosol nitrate photolysis, which increases the model NO2 VCDs by 13 % on an annual basis (25 % in spring) and also increases the air mass factor (AMF) to convert the tropospheric slant column densities (SCDs) inferred from the OMI spectra into VCDs by 7 % on an annual basis (11 % in spring). The increase in the AMF decreases the retrieved NO2 VCDs in the satellite observations, contributing to the improved agreement with the model. Accounting for the 2009–2017 increase in aircraft NOx emissions drives only a 1.4 % mean increase in NO2 VCDs over CONUS and a 2 % increase in the AMF, but the combination of decreasing surface NOx emissions and increasing aircraft emissions is expected to drive a 14 % increase in the AMF over the next decade that will be necessary to account for in the interpretation of satellite NO2 trends. Fire smoke identification with the National Oceanic and Atmospheric Administration (NOAA) Hazard Mapping System (HMS) indicates that wildfires contribute 1 %–8 % of OMI NO2 VCDs over the western US in June–September and that this contribution has been increasing since 2009, contributing to the flattening of OMI NO2 trends. Future analyses of NO2 trends from satellite data to infer trends in surface NOx emissions must critically consider the effects of a rising free tropospheric background due to increasing emissions from aircraft, fires, and possibly lightning.

Prescribed Fires, Smoke Exposure, and Hospital Utilization Among Heart Failure Patients

Background and Aim: Prescribed burns are used for land management, but their environmental health risks have been infrequently studied. We contrasted the health effects of prescribed burns and smoke exposure on heart failure (HF) patients in North Carolina. Methods: We used electronic health records from a North Carolina hospital-based cohort diagnosed with HF between 2014-2016. Prescribed fire data was obtained using the Satellite Mapping Automatic Reanalysis Tool for Fire Incident Reconciliation and the National Emissions Inventory. Smoke density data came from NOAA’s Hazard Mapping System and was linked to patients based on the zip code of residence. Exposures were the number of prescribed burns within 1 km of the primary residence and the number of days of smoke exposure. Outcomes were the number of hospital visits and 30-day readmissions after HF diagnosis. We used a cross-sectional study design and quasi-Poisson models to model the number of hospital visits and zero-inflated Poisson models to model 30-day readmissions. Models were adjusted for age, sex, race, smoking status, urbanicity, poverty, education level, unemployment, median household value, and public assistance, and included an offset for follow-up time. Results presented are the percent change and associated 95% confidence interval (CI). Results: The number of prescribed burns within 1 km of a patient’s residence showed weak evidence for an association with total hospital visits (-4.5%; 95% CI: -13.7%, 5.2%) and was not associated with 30-day readmissions. However, exposure to an additional medium or heavy smoke day was associated with an increase in both hospital visits (1.3%; 95% CI: -0.2%, 2.7%) and 30-day readmissions (5.3%; 95% CI: 2.3%, 8.5%). Conclusions: Smoke exposure is associated with increased hospital utilization among HF patients, but no positive association with prescribed burns was seen. This work does not necessarily represent the views or policies of the US Environmental Protection Agency.

Wildfire Smoke Exposure During Pregnancy and Preterm Birth in California

Background/Aim Owing to climate change and fire suppression policy, wildfires in the Western United States are a significant source of air pollution that threatens to reverse decades of progress in air pollution reduction; effects of wildfires on preterm birth (PTB) are not well understood. We assessed associations between prenatal exposure to wildland fire (wildfire and prescribed fire) smoke and risk of PTB in California. Methods The population included all live singleton births in California, 2007-2015. PTB was defined as gestational age <37 weeks. We assigned wildland fire smoke exposure to geocoded maternal residence at birth with weekly average concentrations of particulate matter ≤2.5 microns attributable to fires from USEPA’s Community Multiscale Air Quality Model (fire PM2.5) and smoke frequencies (smoke days) from National Oceanic and Atmospheric Administration’s Hazard Mapping System. We used logistic regression to estimate odds ratios (OR) for PTB associated with exposures in the final 6 weeks of pregnancy and Cox proportional hazard models to estimate hazard ratios (HR) for PTB in relation to weekly exposures. Models adjusted for maternal age, education, race/ethnicity, season, medical insurance, and smoking. Results Our study included 3,342,267 births. A one-smoke day increase in any of the final 6 weeks of the pregnancy was associated with increased odds of PTB, ranging from OR=1.01 (95%CI:1.01-1.02) in the final week of pregnancy to OR=1.03 (95%CI:1.02-1.03) 5 weeks before birth. A 10µg/m3 increase in fire PM2.5 in the final 6 weeks was associated with ORs ~1.04 (95%CIs covered 1.02-1.06). In survival analyses, HR=1.01 (95%CI:1.01-1.02) for a one-day increase in smoke frequency and HR=1.03 (95%CI:1.02-1.04) for a 10µg/m3 increase in fire PM2.5. Conclusion Wildland fire smoke is modestly associated with PTB. Results can inform efforts to slow climate change, manage forests, and reduce wildfire smoke exposure to pregnant people. Keywords wildfire; pregnancy; preterm birth; climate; air pollution

The impacts of wildfires on ozone production and boundary layer dynamics in California's Central Valley

Abstract. We investigate the role of wildfire smoke on ozone photochemical production (P(O3)) and atmospheric boundary layer (ABL) dynamics in California's Central Valley during June–September from 2016 to 2020. Wildfire events are identified by the Hazard Mapping System (HMS) and the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT). Air quality and meteorological data are analyzed from 10 monitoring sites operated by the California Air Resources Board (CARB) across the Central Valley. On average, wildfires were found to influence air quality in the Central Valley on about 20 % of the total summer days of the study. During wildfire-influenced periods, maximum daily 8 h averaged (MDA8) O3 was enhanced by about 5.5 ppb or 10 % of the median MDA8 (once corrected for the slightly warmer temperatures) over the entire valley. Overall, nearly half of the total exceedances of the National Ambient Air Quality Standards (NAAQS) where MDA8 O3 > 70 ppb occur under the influence of wildfires, and approximately 10 % of those were in exceedance by 5 ppb or less indicating circumstances that would have been in compliance with the NAAQS were it not for wildfire emissions. The photochemical ozone production rate calculated from the modified Leighton relationship was also found to be higher by 50 % on average compared with non-fire periods despite the average diminution of j(NO2) by ∼ 7 % due to the shading effect of the wildfire smoke plumes. Surface heat flux measurements from two AmeriFlux sites in the northern San Joaquin Valley show midday surface buoyancy fluxes decrease by 30 % on average when influenced by wildfire smoke. Similarly, afternoon peak ABL heights measured from a radio acoustic sounding system (RASS) located in Visalia in the southern San Joaquin Valley were found to decrease on average by 80 m (∼ 15 %) with a concomitant reduction of downwelling shortwave radiation of 54 Wm−2, consistent with past observations of the dependence of boundary layer heights on insolation.

An Application of Artificial Neural Network (ANN) for Landslide Hazard Mapping, Susceptibility and Early Warning System: A Review

Landslides are the most common recurrent and prominent natural disaster in Darjeeling hill region. Darjeeling region has been subjected to a number of extreme landslides especially during monsoon that resulted in a significant loss of life and materials. Thus it required to search a solution towards alertness and development to reduce losses connected with natural disaster landslides. The possibility to develop an early warning system is by applying the modern technology. In modern days, the Artificial Neural Network (ANNs) is widely used in multiple domains. These paper studies the effectiveness and efficiency of using Artificial Neural Network (ANNs) in landslides Mapping, Susceptibility and predictions.

Smoke and COVID-19 case fatality ratios during California wildfires

Recent evidence has shown an association between wildfire smoke and COVID-19 cases and deaths. The San Francisco Bay Area, in California (USA), experienced two major concurrent public health threats in 2020: the COVID-19 pandemic and dense smoke emitted by wildfires. This provides a unprecedented context to unravel the role of acute air pollution exposure on COVID-19 severity. A smoke product provided by the National Oceanic and Atmospheric Association Hazard Mapping System was used to identify counties exposed to heavy smoke in summer and fall of 2020. Daily COVID-19 cases and deaths for the United States were downloaded at the County-level from the CDC COVID Data Tracker. Synthetic control methods were used to estimate the causal effect of the wildfire smoke on daily COVID-19 case fatality ratios (CFRs), adjusting for population mobility. Evidence of an impact of wildfire smoke on COVID-19 CFRs was observed, with precise estimates in Alameda and San Francisco. Up to 58 (95% CI: 29, 87) additional deaths for every 1000 COVID-19 incident daily cases attributable to wildfire smoke was estimated in Alameda in early September. Findings indicated that extreme weather events such as wildfires smoke can drive increased vulnerability to infectious diseases, highlighting the need to further study these colliding crises. Understanding the environmental drivers of COVID-19 mortality can be used to protect vulnerable populations from these potentially concomitant public health threats.

Wildfire-related PM₂.₅ and Intensive Care Unit Admissions and Bed Utilization in the United States, 2006-2015

BACKGROUND AND AIM: Wildfire smoke is a growing public health concern in the United States. Numerous studies have documented associations between ambient smoke exposure and severe patient outcomes for single fire seasons or limited geographic regions. However, there are few national-scale health studies of wildfire smoke in the U.S., few studies investigating Intensive Care Unit (ICU) admissions as an outcome, and few specifically framed around hospital operations. This study retrospectively examined the associations between ambient wildfire-related PM₂.₅ at a hospital ZIP code with total hospital ICU admissions using a national-scale hospitalization data set during the years 2006-2015. METHODS: Wildfire smoke was characterized using a combination of kriged PM₂.₅ monitor observations and satellite-derived plume polygons from NOAA’s Hazard Mapping System. ICU admissions data were acquired from Premier, Inc. and encompass 15-20% of all U.S. ICU admissions during the study period. Associations were estimated using a distributed-lag conditional Poisson model under a time-stratified case-crossover design. The impact on ICU admissions and bed utilization of a severe 7-day 120 μg/m³ smoke wave was simulated. RESULTS:We found that a 10 μg/m³ increase in daily wildfire PM₂.₅ was associated with a 2.7% (95% CI: 1.3, 4.1; p=0.00018) increase in ICU admissions five days later. Following the simulated smoke wave, our results predict ICU bed utilization peaking at 131% (95% CI: 43, 239; p0.00001) over baseline. CONCLUSIONS:Our work suggests that hospitals may need to pre-position vital critical care resources when severe smoke events are forecast. KEYWORDS: Intensive Care Unit, Wildfire, Smoke, Particulate Matter, Critical Care

Wildfire exposure {in utero} and use of respiratory medications in early childhood

BACKGROUND AND AIM: Wildfire smoke exposure is a growing concern as wildfires escalate in the western U.S. and abroad (bushfires in Australia). While gestational particulate matter exposure has been linked to increased incidence of childhood respiratory diseases, this association has not been examined with respect to wildfire smoke. Here, we use prescription claims data to investigate the relationship of childhood respiratory illness and in utero exposure to wildfire smoke. METHODS: The cohort consisted of infants (N182,000), born (2010-16) in one of the Metropolitan Statistical Areas (MSAs) of California, Oregon, Washington, Idaho, Nevada, or Montana and followed up to 8 years. We required had continuous prescription coverage and estimable birthdates via claims data in IBM’s MarketScan Commercial Claims Database. Our two outcomes were filled prescriptions of ‘upper respiratory’ and ‘lower respiratory’ medications. Using prescription claims, pediatric respiratory drugs (e.g., fluticasone, albuterol) were classified as ‘upper respiratory’ or ‘lower respiratory’ based on active ingredients, mode of administration, and drug class. Wildfire exposure, classified as number of smoke days during each trimester, for each MSA was determined from National Oceanic Atmospheric Association's Hazard Mapping System. First, second, and third trimesters were defined as 280-197, 196-99, and 1-98 days before estimated birthdate, respectively. We used proportional hazards models to assess the relationship of trimester-specific in utero wildfire smoke exposure and first use of respiratory medication. Models are adjusted for sex, birth season, birthyear, meteorological variables, and seasonality. RESULTS:Preliminary results suggest that gestational wildfire smoke is associated with both upper and lower respiratory medication usage during childhood. These associations are modified by season of birth and may differ by birthyear. Sensitivity analyses will account for preterm birth. CONCLUSIONS:Our results suggest that incidence of childhood respiratory illness, as indicated by respiratory medication usage, is impacted by in utero exposure to wildfire smoke. This abstract does not necessarily represent EPA policy. KEYWORDS: Air pollution, Wildfires, Children's environmental health, Respiratory outcomes, Natural disasters, Climate

Short-Term PM2.5 Exposure Impacts Cognitive Performance: A Longitudinal Repeated Measures Study of the Western US 2017-2018

BACKGROUND AND AIM: There is increasing evidence that air pollution exposure adversely impacts cognitive health. While wildfire smoke is a pollutant of growing concern, there is little evidence to date if short-term exposure impacts cognitive function. Using repeated measures, we aimed to identify and quantify the relationship between daily and sub-daily PM₂.₅ and smoke exposure and changes in cognitive performance in adults. METHODS: Cognitive performance data was obtained for 1,467 Lumosity users, age 18+, who completed 20 plays of a brain-training game targeted to improve cognitive flexibility in the western United States during 2017-2018. We considered two measures of daily and sub-daily air pollution exposure: (1) PM₂.₅ concentration estimates, obtained from a Bayesian Maximum Entropy data fusion of observations from FRM/FEM and PurpleAir monitors, and (2) smoke density, obtained from the NOAA Hazard Mapping System. We used a longitudinal repeated measures study design with an autoregressive linear mixed effects model to quantify the relationships between measures of short-term exposure and cognitive performance, overall and by age group. RESULTS:A 10 μg/m³ increase in daily average PM₂.₅ in the week prior to play was associated with 46.9 (95% CI: -92.9, -0.9) point decrease in Lumosity game score. The highest impacts were observed in the youngest (18-29) and oldest (70+) age groups. The presence of medium smoke density on the day of game play was associated with a significant decrease in score. No other measures of smoke density in the 2 weeks prior to play were associated with Lumosity performance. No associations between measures of sub-daily PM₂.₅ exposure and performance were observed. CONCLUSIONS:Results indicate that increases in daily average PM₂.₅ can adversely impact cognitive flexibility. Short-term smoke exposure may also be associated with cognitive performance, but more research is needed to investigate this relationship. This abstract does not reflect EPA policy. KEYWORDS: Particulate matter, Short-term exposure, Neurodevelopmental outcomes, Wildfires, Environmental epidemiology

Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm

This study performs flood hazard mapping and evaluates community flood coping strategies. In addition, it proposes a humanitarian aid information system (HAIS) to enhance emergency support for flood victims. First, a flood hazard map was prepared using the hydrodynamic model (HM)–FLO 2D coupled with a machine learning algorithm (MLA)-scaled conjugate gradient neural network (SCG-NN). The performance of the MLA was evaluated using a validation dataset and statistical measures such as the mean square error (MSE: 0.080), root mean square error (RMSE: 0.282), and coefficient of determination (R2 = 0.830). According to the generated flood hazard map, most of the study area was classified as low (47.85%) or moderate (27.47%) hazardous zones, whereas only a small portion was delineated as high (20.64%) or very high (4.04%) hazardous zones. The accuracy of the hazard map (HM-MLA) versus the ground truth was tested statistically and was found to be high. Second, an investigation of local flood management strategies revealed that the current information system is not well prepared for emergencies, including the quantification of emergency relief necessities. Therefore, an HAIS, which specifies hazard information and quantifies emergency aids (food items) for flood victims, can be an effective emergency preparedness tool. We calculated the required emergency aid considering satellite-derived flood data. Finally, we conclude that the proposed HAIS will help humanitarian organizations and government agencies coordinate and perform relief operations effectively in the worst-hit regions across the country.

Associations Between Wildfire-Related PM2.5 and Intensive Care Unit Admissions in the United States, 2006-2015.

Wildfire smoke is a growing public health concern in the United States. Numerous studies have documented associations between ambient smoke exposure and severe patient outcomes for single‐fire seasons or limited geographic regions. However, there are few national‐scale health studies of wildfire smoke in the United States, few studies investigating Intensive Care Unit (ICU) admissions as an outcome, and few specifically framed around hospital operations. This study retrospectively examined the associations between ambient wildfire‐related PM2.5 at a hospital ZIP code with total hospital ICU admissions using a national‐scale hospitalization data set. Wildfire smoke was characterized using a combination of kriged PM2.5 monitor observations and satellite‐derived plume polygons from National Oceanic and Atmospheric Administration's Hazard Mapping System. ICU admissions data were acquired from Premier, Inc. and encompass 15%–20% of all U.S. ICU admissions during the study period. Associations were estimated using a distributed‐lag conditional Poisson model under a time‐stratified case‐crossover design. We found that a 10 μg/m3 increase in daily wildfire PM2.5 was associated with a 2.7% (95% CI: 1.3, 4.1; p = 0.00018) increase in ICU admissions 5 days later. Under stratification, positive associations were found among patients aged 0–20 and 60+, patients living in the Midwest Census Region, patients admitted in the years 2013–2015, and non‐Black patients, though other results were mixed. Following a simulated severe 7‐day 120 μg/m3 smoke event, our results predict ICU bed utilization peaking at 131% (95% CI: 43, 239; p < 10−5) over baseline. Our work suggests that hospitals may need to preposition vital critical care resources when severe smoke events are forecast.

Framework of Emergency Response System for Potential Large-Scale Landslide in Taiwan

In order to lower the risks of large-scale landslides and improve community resilience in Taiwan, a long-term project has been promoted by the Soil and Water Conservation Bureau since 2017. In this study, methods to build an emergency response framework including hazard mapping and early warning system establishment were introduced. For hazard mapping, large-scale landslides were categorized into a landslide, debris flow, or landslide dam type based on the movement of unstable materials and topography. Each disaster type has different hazard zone delineation methods to identify the affected areas. After establishing the possible effected areas, early warning mechanisms, including warning value using rainfall as the indicator and evacuation procedures, should be created for emergency response. To set the warning value, analysis of the occurrence thresholds of previous existing large-scale landslides was conducted to determine the critical rainfall and further utilized to set the warning value considering the evacuation time for the locals. Finally, for integration with the current debris flow emergency response system, potential large-scale landslide areas were further divided into two types based on their spatial relationship with debris flows. For those overlapping with existing debris flow protected targets, the current emergency response system was upgraded considering the impact of large-scale landslides, while the others were suggested for use in building a new emergency response procedure. This integrated framework could provide a feasible risk avoidance method for local government and residents.