Category: Papers

Vu et al.: The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru

Vu, B. N., Tapia, V., Ebelt, S., Gonzales, G. F., Liu, Y., Steenland, K. (2021). The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru. Environmental Research, 199, 111226. 

Elsevier: Link 

Lima, Peru is one of the most populated and polluted cities in South America. However, epidemiological studies pertaining to air pollution in Lima, Peru have been limited by the lack of quality and consistent historical ground measurements. With the recent development of exposure estimates created by Vu et al.’s satellite-driven machine learning model, we conduct a time-series study to investigate the association between PM2.5 and asthma emergency department (ED) visits between 2010 to 2016. Results from this study showed that from the 103,974 cases of asthma during the study period across Lima, there was a 3.7% increase in ED visits for every 6.02 µg/m3 (IQR, interquartile range) increase in PM2.5 for same day exposure. When stratified by age,  children ages 18 and younger saw a 4.5% increase in ED visits for every IQR increase in PM2.5 while adults ages 19-64 saw a 6.0% increase in ED visits for every IQR increase in PM2.5. The elderly population saw a 16.0% decrease in ED visits for every IQR increase in PM2.5; however, this age group had a small population size and the models may not be robust. Results from this study provides additional literature on the use of satellite-derived exposure estimates in epidemiologic studies conducted in low- and middle-income countries.

 

 

 

Bi et al.: Characterizing outdoor infiltration and indoor contribution of PM2.5 with PurpleAir data

Bi, J., Wallace, L. A., Sarnat, J. A., & Liu, Y. (2021). Characterizing outdoor infiltration and indoor contribution of PM2.5 with citizen-based low-cost monitoring data. Environmental Pollution, 276, 116763.

ResearchGate (full text): Link       Elsevier: Link

Research in quantifying infiltration factors (Finf), the fraction of outdoor PM2.5 that infiltrates indoors, has been historically limited in space and time due to the high costs of monitor deployment and maintenance. Recently, the growth of openly accessible PM2.5 measurements provides an unprecedented opportunity to characterize Finf at large spatiotemporal scales. In this analysis, 91 consumer-grade PurpleAir indoor/outdoor monitor pairs were identified in California (41 residential houses and 50 public/commercial buildings) during a 20-month period in 2019 – 2020 with around 650k hours of paired PM2.5 measurements. An empirical method was developed based on local polynomial regression to estimate site-specific Finf. The estimated Finf had a mean of 0.26 (25th, 75th percentiles: [0.15, 0.34]) with a mean bootstrap standard deviation of 0.04. The resulting exposure errors (differences between total indoor exposure and exposure due to particles of ambient origin) were plotted as a function of the ambient concentration for the first time. The peak of the exposure errors occurred at low outdoor concentrations < 5 µg/m3 and dropped to nearly zero near 30 µg/m3.

 

 

Liang et al.: The 17-y spatiotemporal trend of PM2.5 and its mortality burden in China

Estimation of the chronic health effects of PM2.5 exposure has been hindered by the lack of long-term PM2.5 data in China. To support this, high-performance machine-learning models were developed to estimate PM2.5 concentrations at 1-km resolution in China from 2000 to 2016, based on satellite data, meteorological conditions, land cover information, road networks, and air pollution emission indicators. By adopting imputation techniques, relatively unbiased spatiotemporally continuous exposure estimates were generated. Annual mortality burdens attributable to long-term PM2.5 exposure were estimated at the provincial scale, and the national total adult premature deaths were estimated at 30.8 million over the 17-y period in China.

This study was published in PNAS (link).

 

Wang et al.: Satellite-based assessment of the long-term efficacy of PM2.5 pollution control policies across the Taiwan Strait

Evaluating the efficacy of air pollution control policies is an essential part of the decision-making process to develop new policies and improve existing measures.  In this analysis, we assessed the effects air pollution control policies in the Taiwan Strait Region from 2005 to 2018 using full-coverage, high-resolution PM2.5generated by a satellite-driven machine learning model. A ten-fold cross-validation for our prediction model showed an R2value of 0.89, demonstrating that these predictions can be used for policy evaluation. During the 14-year period, PM2.5levels in all areas of Fujian and Taiwan underwent a significant decrease. Separate regression models for policy evaluation in Taiwan and Fujian showed that all considered policies have mitigated PM2.5pollution to various degrees. The Clean Air Action Plans (CAAP) is the most effective control policy in Taiwan, while the Action Plan of Air Pollution Prevention and Control (APPC-AP) and Three-year Action Plan for Blue Skies (3YAP-BS) as well as their provincial implementation plans are the most successful in Fujian. The effectiveness of control policies, however, varies by land-use types especially for Taiwan.

 

 

Bi et al.: Temporal changes in short-term associations between cardiorespiratory ED visits and PM2.5

Temporal changes in short-term associations between cardiorespiratory emergency department visits and PM2.5 in Los Angeles, 2005 to 2016

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 (as a mixture). In this analysis, we examined temporal changes in the risk of emergency department (ED) visits for cardiovascular diseases (CVDs) and asthma associated with short-term exposure to PM2.5 in Los Angeles from 2005 to 2016, a period in which the implementation of emission control programs had led to changes in PM2.5 concentration and composition. We observed significant changes in the risk of CVD (with an increased risk) and asthma (with a decreased risk) ED visits when PM2.5 concentrations decreased over time. The observed changes in risk could be related to changes in PM2.5 composition (e.g., an increased fraction of organic carbon and a decreased fraction of sulfate) and other factors such as improvements in healthcare and differential exposure misclassification. 

Bi, J., D’Souza, R. R., Rich, D. Q., Hopke, P. K., Russell, A. G., Liu, Y., Chang, H. H., & Ebelt, S. (2020). Temporal changes in short-term associations between cardiorespiratory emergency department visits and PM2.5 in Los Angeles, 2005 to 2016. Environmental Research, 190, 109967. 10.1016/j.envres.2020.109967.

 

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

In the article, the authors focus on a region where traditional satellite AOD models have not performed as well compared to other areas of the US, in order to determine which region-specific parameters have the highest impact on model accuracy. Using a two-stage linear approach, the authors identified important meteorological and land use parameters including temperature, relative humidity, wind, road distance, distance to coast and other local factors. Of note, a variable representing the ratio of PM2.5 to PM10 particles was included to account for airborne dust. This parameter is not generally used in other regional analyses and was an important addition to the Southern California model, improving accuracy from R2 of 0.70 to 0.80. This study adds significantly to the current body of research in the region of the Southwestern US, suggesting that the influence of PM10 to AOD should be considered in areas with high concentrations of airborne dust.

Geng et al.: Random forest models for PM2.5 speciation using MISR data

Random forest models were developed to predict ground-level daily PM2.5 speciation concentrations in California from MISR fractional AODs and other supporting data such as ground measurements, chemical transport model simulations, land use variables and meteorological fields. Sensitivity tests were also conducted to explore the influence of variable selection on model performance. Results shows that fractional AODs and total AOD have similar predicting power in estimating PM2.5 species if there are sufficient ground measurements and predictor data to support the most sophisticated model structure. Otherwise, models using fractional AODs outperform those with total AOD. PM2.5 speciation concentrations are more sensitive to land use variables than other supporting data such as CTM simulations and meteorological information.

This article was published in Environmental Research Letters in March 2020.

 

Bi et al.: Incorporating low-cost sensor data into large-scale, high-resolution PM2.5 modeling

Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale

A spatially varying calibration and a down-weighting strategy have been proposed to incorporate volunteer-generated low-cost air quality data from PurpleAir sensors into large-scale PM2.5 exposure assessment while minimizing the negative impacts of their significant uncertainty. The inclusion of PurpleAir measurements allowed the exposure estimates to better reflect PM2.5 spatial details and hotspots such as wildfire smokes. The proposed calibration and modeling strategies can be used in regions with insufficient reference-grade measurements to improve air pollution exposure assessment.

Bi, J., Wildani, A., Chang, H. H., & Liu, Y. (2020). Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at A Large Spatial Scale. Environmental Science & Technology. 10.1021/acs.est.9b06046

Bi et al.: Contribution of low-cost sensor measurements to the prediction of PM2.5 levels

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA

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. In this study, the contribution of low-cost sensor measurements to high-resolution PM2.5 prediction was evaluated in an exemplary region – Imperial County in California – with a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study shows that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 modeling, while the remaining uncertainty in calibrated sensor measurements still caused apparent outliers in the prediction results. This study highlights the need for more effective calibration or integration methods to relieve the negative impact of remaining sensor uncertainty.

Bi, J., Stowell, J., Seto, E.Y., English, P.B., Al-Hamdan, M.Z., Kinney, P.L., Freedman, F.R. & Liu, Y. (2020). Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA. Environmental Research, p.108810. 10.1016/j.envres.2019.108810

 

She et al.: Hourly PM2.5 levels during heavy winter episodes in the Yangtze River Delta

In this publication, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding monitoring PM2.5 concentrations, meteorological and land use data on a 6-km modeling grid with complete coverage in time and space. The 10-fold cross-validation R2 was 0.72 with a regression slope of 1.01 between observed and predicted hourly PM2.5 concentrations. We further analyzed two representative large regional episodes, i.e., a “multi-process diffusion episode” during December 21–26, 2015 and a “Chinese New Year episode” during February 7–8, 2016. We concluded that AOD retrieved by geostationary satellites could serve as a new valuable data source for analyzing the heavy air pollution episodes.

 

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