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1.
Environ Sci Technol ; 57(1): 440-450, 2023 01 10.
Artigo em Inglês | MEDLINE | ID: mdl-36508743

RESUMO

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Dióxido de Nitrogênio/análise , Dióxido de Carbono , Monitoramento Ambiental , Poluição do Ar/análise , Material Particulado/análise , Fuligem/análise
2.
Environ Sci Technol ; 57(48): 19990-19998, 2023 Dec 05.
Artigo em Inglês | MEDLINE | ID: mdl-37943716

RESUMO

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 PM2.5 concentrations attributed to both fire smoke and nonsmoke sources across the contiguous U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke contributed over 25% of daily PM2.5 concentrations at ∼40% of all regulatory air monitors in the EPA's air quality system (AQS) for more than one month per year. People residing outside the vicinity of an EPA AQS monitor (defined by a 5 km radius) were subject to 36% more smoke impact days compared with those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM2.5 concentrations to between 9 and 10 µg/m3 would result in approximately 35-49% of the AQS monitors falling in nonattainment areas, taking into account the impact of fire smoke. If fire smoke contribution is excluded, this percentage would be reduced by 6 and 9%, demonstrating the significant negative impact of wildland fires on air quality.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Incêndios , Incêndios Florestais , Estados Unidos , Humanos , Poluentes Atmosféricos/análise , Fumaça/análise , Poluição do Ar/análise , Sudeste dos Estados Unidos , Material Particulado
3.
Environ Res ; 223: 115451, 2023 04 15.
Artigo em Inglês | MEDLINE | ID: mdl-36764437

RESUMO

BACKGROUND: Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms. OBJECTIVES: We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies. METHODS: We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs. RESULTS: Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility. DISCUSSION: Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Humanos , Material Particulado/análise , Monitoramento Ambiental/métodos , Poluição do Ar/análise , Poluentes Atmosféricos/análise , Características de Residência
4.
Environ Sci Technol ; 56(3): 1544-1556, 2022 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-35019267

RESUMO

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods rely on either chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product, NASA's Goddard Earth Observing System "Composition Forecasting" (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next 5 days at a 1 km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next 2 days had an overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following 3 forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with a validation normalized mean bias close to 0, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monitoramento Ambiental/métodos , Aprendizado de Máquina , Material Particulado/análise
5.
Remote Sens Environ ; 2712022 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-37033879

RESUMO

Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)'s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 µg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 µg/m3) and 0.73 (16.58 µg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 µg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 µg/m3) and 0.79 (11.89 µg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 µg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 µg/m3) and 0.85 (14.88 µg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode.

6.
Environ Health ; 20(1): 93, 2021 08 23.
Artigo em Inglês | MEDLINE | ID: mdl-34425829

RESUMO

BACKGROUND: Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model. METHODS: We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM2.5 dataset. RESULTS: For all PM2.5 datasets, we observed positive associations between PM2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m3 increase in daily PM2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available. CONCLUSIONS: Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM2.5 and CVD admissions, regardless of model choice.


Assuntos
Poluentes Atmosféricos/efeitos adversos , Doenças Cardiovasculares/epidemiologia , Exposição Ambiental/efeitos adversos , Hospitalização/estatística & dados numéricos , Modelos Teóricos , Material Particulado/efeitos adversos , Poluentes Atmosféricos/análise , Exposição Ambiental/análise , Humanos , New York/epidemiologia , Material Particulado/análise
7.
Remote Sens Environ ; 2662021 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-34776543

RESUMO

Exposure to fine particulate matter (PM2.5) has been linked to a substantial disease burden globally, yet little has been done to estimate the population health risks of PM2.5 in South Africa due to the lack of high-resolution PM2.5 exposure estimates. We developed a random forest model to estimate daily PM2.5 concentrations at 1 km2 resolution in and around industrialized Gauteng Province, South Africa, by combining satellite aerosol optical depth (AOD), meteorology, land use, and socioeconomic data. We then compared PM2.5 concentrations in the study domain before and after the implementation of the new national air quality standards. We aimed to test whether machine learning models are suitable for regions with sparse ground observations such as South Africa and which predictors played important roles in PM2.5 modeling. The cross-validation R2 and Root Mean Square Error of our model was 0.80 and 9.40 µg/m3, respectively. Satellite AOD, seasonal indicator, total precipitation, and population were among the most important predictors. Model-estimated PM2.5 levels successfully captured the temporal pattern recorded by ground observations. Spatially, the highest annual PM2.5 concentration appeared in central and northern Gauteng, including northern Johannesburg and the city of Tshwane. Since the 2016 changes in national PM2.5 standards, PM2.5 concentrations have decreased in most of our study region, although levels in Johannesburg and its surrounding areas have remained relatively constant. This is anadvanced PM2.5 model for South Africa with high prediction accuracy at the daily level and at a relatively high spatial resolution. Our study provided a reference for predictor selection, and our results can be used for a variety of purposes, including epidemiological research, burden of disease assessments, and policy evaluation.

8.
Environ Sci Technol ; 54(4): 2152-2162, 2020 02 18.
Artigo em Inglês | MEDLINE | ID: mdl-31927908

RESUMO

Low-cost air quality sensors are promising supplements to regulatory monitors for PM2.5 exposure assessment. However, little has been done to incorporate the low-cost sensor measurements in large-scale PM2.5 exposure modeling. We conducted spatially varying calibration and developed a downweighting strategy to optimize the use of low-cost sensor data in PM2.5 estimation. In California, PurpleAir low-cost sensors were paired with air quality system (AQS) regulatory stations, and calibration of the sensors was performed by geographically weighted regression. The calibrated PurpleAir measurements were then given lower weights according to their residual errors and fused with AQS measurements into a random forest model to generate 1 km daily PM2.5 estimates. The calibration reduced PurpleAir's systematic bias to ∼0 µg/m3 and residual errors by 36%. Increased sensor bias was found to be associated with higher temperature and humidity, as well as longer operating time. The weighted prediction model outperformed the AQS-based prediction model with an improved random cross-validation (CV) R2 of 0.86, an improved spatial CV R2 of 0.81, and a lower prediction error. The temporal CV R2 did not improve due to the temporal discontinuity of PurpleAir. The inclusion of PurpleAir data allowed the predictions to better reflect PM2.5 spatial details and hotspots.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , California , Monitoramento Ambiental , Material Particulado
9.
Environ Res ; 190: 109967, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32810677

RESUMO

BACKGROUND: Emissions control programs targeting certain air pollution sources may alter PM2.5 composition, as well as the risk of adverse health outcomes associated with PM2.5. OBJECTIVES: We examined temporal changes in the risk of emergency department (ED) visits for cardiovascular diseases (CVDs) and asthma associated with short-term increases in ambient PM2.5 concentrations in Los Angeles, California. METHODS: Poisson log-linear models with unconstrained distributed exposure lags were used to estimate the risk of CVD and asthma ED visits associated with short-term increases in daily PM2.5 concentrations, controlling for temporal and meteorological confounders. The models were run separately for three predefined time periods, which were selected based on the implementation of multiple emissions control programs (EARLY: 2005-2008; MIDDLE: 2009-2012; LATE: 2013-2016). Two-pollutant models with individual PM2.5 components and the remaining PM2.5 mass were also considered to assess the influence of changes in PM2.5 composition on changes in the risk of CVD and asthma ED visits associated with PM2.5 over time. RESULTS: The relative risk of CVD ED visits associated with a 10 µg/m3 increase in 4-day PM2.5 concentration (lag 0-3) was higher in the LATE period (rate ratio = 1.020, 95% confidence interval = [1.010, 1.030]) compared to the EARLY period (1.003, [0.996, 1.010]). In contrast, for asthma, relative risk estimates were largest in the EARLY period (1.018, [1.006, 1.029]), but smaller in the following periods. Similar temporal differences in relative risk estimates for CVD and asthma were observed among different age and season groups. No single component was identified as an obvious contributor to the changing risk estimates over time, and some components exhibited different temporal patterns in risk estimates from PM2.5 total mass, such as a decreased risk of CVD ED visits associated with sulfate over time. CONCLUSIONS: Temporal changes in the risk of CVD and asthma ED visits associated with short-term increases in ambient PM2.5 concentrations were observed. These changes could be related to changes in PM2.5 composition (e.g., an increasing fraction of organic carbon and a decreasing fraction of sulfate in PM2.5). Other factors such as improvements in healthcare and differential exposure misclassification might also contribute to the changes.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/efeitos adversos , Poluentes Atmosféricos/análise , Poluição do Ar/efeitos adversos , Poluição do Ar/análise , Serviço Hospitalar de Emergência , Los Angeles/epidemiologia , Material Particulado/efeitos adversos , Material Particulado/análise
10.
Environ Res ; 180: 108810, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31630004

RESUMO

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Monitoramento Ambiental , California , Monitoramento Ambiental/economia , Modelos Estatísticos , Material Particulado
11.
Remote Sens Environ ; 221: 665-674, 2019 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31359889

RESUMO

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM2.5) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD values. As a result, the fully covered and unbiased PM2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM2.5 and made full- coverage PM2.5 predictions by considering these impacts. To estimate missing AOD values, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The "out-of-bag" R2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R2 of 0.82. In the gap-filling models, the snow fraction was of higher significance to the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD data for predicting PM2.5 levels with high resolutions and complete coverage.

12.
Sci Total Environ ; 925: 171652, 2024 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-38485010

RESUMO

Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure - especially near major roads in the U.S.

13.
Artigo em Inglês | MEDLINE | ID: mdl-38589565

RESUMO

BACKGROUND: Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. OBJECTIVE: Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. METHODS: We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. RESULTS: The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- R 2 = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and R 2 = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- R 2 = 0.51 (with LCS). IMPACT: We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

14.
Environ Pollut ; 343: 123227, 2024 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-38147948

RESUMO

Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 µg/m3]) compared to the model with all LCM measurements (0.84 [0.9 µg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 µg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 µg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 µg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Material Particulado/análise , Monitoramento Ambiental/métodos , Poluição do Ar/análise , Projetos de Pesquisa
15.
Res Sq ; 2023 Sep 12.
Artigo em Inglês | MEDLINE | ID: mdl-37790383

RESUMO

As wildfires become more frequent and intense, fire smoke has significantly worsened 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 PM2.5 concentrations attributed to both fire smoke and non-smoke sources across the Continental U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke affected daily PM2.5 concentrations at 40% of all regulatory air monitors in EPA's Air Quality System (AQS) for more than one month each year. People residing outside the vicinity of an EPA AQS monitor were subject to 36% more smoke impact days compared to those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM2.5 concentrations to between 9 and 10 µg/m3 would result in approximately 29% to 40% of the AQS monitors falling in nonattainment areas without taking into account the contribution from fire smoke. When fire smoke impact is considered, this percentage would rise to 35% to 49%, demonstrating the significant negative impact of wildfires on air quality.

16.
Environ Health Perspect ; 131(4): 47003, 2023 04.
Artigo em Inglês | MEDLINE | ID: mdl-37011135

RESUMO

BACKGROUND: Previous studies of short-term ambient air pollution exposure and asthma morbidity in the United States have been limited to a small number of cities and/or pollutants and with limited consideration of effects across ages. OBJECTIVES: To estimate acute age group-specific effects of fine and coarse particulate matter (PM), major PM components, and gaseous pollutants on emergency department (ED) visits for asthma during 2005-2014 across the United States. METHODS: We acquired ED visit and air quality data in regions surrounding 53 speciation sites in 10 states. We used quasi-Poisson log-linear time-series models with unconstrained distributed exposure lags to estimate site-specific acute effects of air pollution on asthma ED visits overall and by age group (1-4, 5-17, 18-49, 50-64, and 65+ y), controlling for meteorology, time trends, and influenza activity. We then used a Bayesian hierarchical model to estimate pooled associations from site-specific associations. RESULTS: Our analysis included 3.19 million asthma ED visits. We observed positive associations for multiday cumulative exposure to all air pollutants examined [e.g., 8-d exposure to PM2.5: rate ratio of 1.016 with 95% credible interval (CI) of (1.008, 1.025) per 6.3-µg/m3 increase, PM10-2.5: 1.014 (95% CI: 1.007, 1.020) per 9.6-µg/m3 increase, organic carbon: 1.016 (95% CI: 1.009, 1.024) per 2.8-µg/m3 increase, and ozone: 1.008 (95% CI: 0.995, 1.022) per 0.02-ppm increase]. PM2.5 and ozone showed stronger effects at shorter lags, whereas associations of traffic-related pollutants (e.g., elemental carbon and oxides of nitrogen) were generally stronger at longer lags. Most pollutants had more pronounced effects on children (<18 y old) than adults; PM2.5 had strong effects on both children and the elderly (>64 y old); and ozone had stronger effects on adults than children. CONCLUSIONS: We reported positive associations between short-term air pollution exposure and increased rates of asthma ED visits. We found that air pollution exposure posed a higher risk for children and older populations. https://doi.org/10.1289/EHP11661.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Asma , Poluentes Ambientais , Ozônio , Criança , Adulto , Humanos , Estados Unidos/epidemiologia , Idoso , Teorema de Bayes , Poluição do Ar/análise , Poluentes Atmosféricos/análise , Asma/epidemiologia , Material Particulado/análise , Ozônio/análise , Serviço Hospitalar de Emergência
17.
Environ Int ; 158: 106917, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-34624589

RESUMO

Estimating ground-level ozone concentrations is crucial to study the adverse health effects of ozone exposure and better understand the impacts of ground-level ozone on biodiversity and vegetation. However, few studies have attempted to use satellite retrieved ozone as an indicator given their low sensitivity in the boundary layer. Using the Troposphere Monitoring Instrument (TROPOMI)'s total ozone column together with the ozone profile information retrieved by the Ozone Monitoring Instrument (OMI), as TROPOMI ozone profile product has not been released, we developed a machine learning model to estimate daily maximum 8-hour average ground-level ozone concentration at 10 km spatial resolution in California. In addition to satellite parameters, we included meteorological fields from the High-Resolution Rapid Refresh (HRRR) system at 3 km resolution and land-use information as predictors. Our model achieved an overall 10-fold cross-validation (CV) R2 of 0.84 with root mean square error (RMSE) of 0.0059 ppm, indicating a good agreement between model predictions and observations. Model predictions showed that the suburb of Los Angeles Metropolitan area had the highest ozone levels, while the Bay Area and the Pacific coast had the lowest. High ozone levels are also seen in Southern California and along the east side of the Central Valley. TROPOMI data improved the estimate of extreme values when compared to a similar model without it. Our study demonstrates the feasibility and value of using TROPOMI data in the spatiotemporal characterization of ground-level ozone concentration.


Assuntos
Poluentes Atmosféricos , Ozônio , Poluentes Atmosféricos/análise , Monitoramento Ambiental , Los Angeles , Aprendizado de Máquina , Meteorologia , Ozônio/análise
18.
Environ Health Perspect ; 130(2): 27004, 2022 02.
Artigo em Inglês | MEDLINE | ID: mdl-35138921

RESUMO

BACKGROUND: Although short-term ozone (O3) exposure has been associated with a series of adverse health outcomes, research on the health effects of chronic O3 exposure is still limited, especially in developing countries because of the lack of long-term exposure estimates. OBJECTIVES: The present study aimed to estimate the spatiotemporal distribution of monthly mean daily maximum 8-h average O3 concentrations in China from 2005 to 2019 at a 0.05° spatial resolution. METHODS: We developed a machine learning model with a satellite-derived boundary-layer O3 column, O3 precursors, meteorological conditions, land-use information, and proxies of anthropogenic emissions as predictors. RESULTS: The random, spatial, and temporal cross-validation R2 of our model were 0.87, 0.86, and 0.76, respectively. Model-predicted spatial distribution of ground-level O3 concentrations showed significant differences across seasons. The highest summer peak of O3 occurred in the North China Plain, whereas southern regions were the most polluted in winter. Most large urban centers showed elevated O3 levels, but their surrounding suburban areas may have even higher O3 concentrations owing to nitrogen oxides titration. The annual trend of O3 concentrations fluctuated over 2005-2013, but a significant nationwide increase was observed afterward. DISCUSSION: The present model had enhanced performance in predicting ground-level O3 concentrations in China. This national data set of O3 concentrations would facilitate epidemiological studies to investigate the long-term health effect of O3 in China. Our results also highlight the importance of controlling O3 in China's next round of the Air Pollution Prevention and Control Action Plan. https://doi.org/10.1289/EHP9406.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Ozônio , Poluentes Atmosféricos/análise , Poluição do Ar/análise , China/epidemiologia , Monitoramento Ambiental/métodos , Ozônio/análise , Estações do Ano
19.
Environ Int ; 158: 106897, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-34601393

RESUMO

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM2.5 in epidemiological studies. Using sparse regulatory PM2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models' accuracy in predicting PM2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort's residential locations). In this study, we used the PM2.5 measurements from a publicly available commercial low-cost PM2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 "gold-standard" monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R2 and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 µg/m3 to 0.92 and 1.63 µg/m3, respectively. The external spatial validation R2 and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 µg/m3 to 0.79 and 0.88 µg/m3, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model's prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monitoramento Ambiental , Estudos Epidemiológicos , Humanos , Material Particulado/análise
20.
Environ Health Perspect ; 130(9): 97008, 2022 09.
Artigo em Inglês | MEDLINE | ID: mdl-36169978

RESUMO

BACKGROUND: Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations. OBJECTIVES: To develop a daily, high-resolution ambient CO exposure prediction model at the city scale. METHODS: We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations. RESULTS: The CO model had spatial cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R2 and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads. DISCUSSION: The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monóxido de Carbono , Monitoramento Ambiental , Humanos , Material Particulado/análise
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