Comparison of Exposure Estimation Methods for Air Pollutants: Ambient Monitoring Data and Regional Air Quality Simulation

Bravo, Mercedes A.; Fuentes, Montserrat; Zhang, Yang; Burr, M.J.; Bell, Michelle L.

doi:10.1289/isee.2011.00073

Cited by 3 publications

(5 citation statements)

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“…Finally, Just et al developed a more sophisticated mixed-effects regression model, including AOD satellite measurements as their primary PM 2.5 predictor, over a period of 10 years (Just et al, 2015). While innovative, this method is limited mainly in that it is inapplicable in areas where little or no PM 2.5 monitoring data exist for its validation, and no satellite measurements can be taken on cloudy days (Bravo et al, 2012;Paciorek and Liu, 2009). We overcame the limitation related to unavailable entry data, an obstacle faced by many developing countries, by using information on predictors from secondary sources that are easy to access and interpret and are permanently monitored.…”

Section: Discussionmentioning

confidence: 99%

Geocoding and spatio-temporal modeling of long-term PM2.5 and NO2 exposure: the Mexican Teachers' Cohort

Cervantes-Martínez¹,

Horacio²,

Carlos

et al. 2021

Preprint

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Epidemiological studies on the effects of air pollution in Mexico often use the environmental concentrations of monitors closest to the home as exposure proxies, yet this approach disregards the space gradients of pollutants and assumes that individuals have no intra-city mobility. Our aim was to develop high-resolution spatial and temporal models for predicting long-term exposure to PM2.5 and NO2 in a population of ~ 16 500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants. Using information from secondary sources on geographic and meteorological variables as well as other pollutants, we fitted two generalized additive models to predict monthly PM2.5 and NO2 concentrations in the 2004–2019 period. The models were evaluated through 10-fold cross validation. Both showed high predictive accuracy with out-of-sample data and no overfitting (CV RMSE = 0.102 for PM2.5 and CV RMSE = 4.497 for NO2). Participants were exposed to a monthly average of 24.38 (6.78) µg/m3 of PM2.5 and 28.21 (8.00) ppb of NO2 during the study period. These models offer a solid alternative for estimating PM2.5 and NO2 exposure with high spatio-temporal resolution for epidemiological studies in the Valle de México region.

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Section: Discussionmentioning

confidence: 99%

Geocoding and spatio-temporal modeling of long-term PM2.5 and NO2 exposure: the Mexican Teachers' Cohort

Cervantes-Martínez¹,

Horacio²,

Carlos

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…The principle of air pollution occurs in the air when there is a pollutant element that comes from natural activity and also from the human activities. Air pollution can affect the balance of air quality and disruption for the survival of humans and animals [4].…”

Section: Air Pollution Datamentioning

confidence: 99%

“…The API value is calculated by all parameter. The kind of air pollution parameters are Nitrogen Dioxide (NO2), Sulfur Dioxide (sO2), Carbon Monoxide (CO), Particulate Matter (PM10), and Ozone (O3) [4]. This API value is obtained for each air quality parameters.…”

Section: Air Pollution Datamentioning

confidence: 99%

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Comparison of Prediction Methods for Air Pollution Data in Malaysia and Singapore

2018

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The process for analyzing and extracting useful information from a large database that employs one or more machine learning techniques is Data Mining. There are many data mining methods that can be used in a variety of data patterns. One of them is prediction modeling. This study compares several data mining performance methods for prediction such as Naïve Bayes, Random Tree, J48, and Rough Set to get the most powerful classifier to extract the knowledge of air pollution data. The parameters being used for observation in the performance of the prediction methods are correctly and incorrectly classified instances, the time taken, and kappa statistic. The experimental result reveals that Rough Set is extremely good for classifying the Air Pollutant Index (API) data from Malaysia and Singapore. Rough Set has the lowest error and the highest performance compared to other methods with the accuracy more than 97%.

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“…Chemical transport models (CTMs) have recently been used as one of the alternative approaches to address the limitations of central site monitors (Anenberg et al, 2010;Bravo et al, 2012;Sarnat et al, 2011;Tainio et al, 2013). The latest generation of CTMs represents a "state-of-the-science" understanding of emissions, transport, and atmospheric chemistry.…”

Section: Introductionmentioning

confidence: 99%

Long-term particulate matter modeling for health effects studies in California – Part 1: Model performance on temporal and spatial variations

Hu¹,

Zhang²,

Ying³

et al. 2014

Preprint

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Abstract. For the first time, a decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution and daily time resolution has been conducted in California to provide air quality data for health effects studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, EC, OC, nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95%, 100%, 71%, 73%, and 92% of the simulated months, respectively. The base dataset provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated one day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to over-predict wind speed during stagnation events, leading to under-predictions of high PM concentrations, usually in winter months. The WRF model also generally under-predicts relative humidity, resulting in less particulate nitrate formation especially during winter months. These issues will be improved in future studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/.

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Comparison of Exposure Estimation Methods for Air Pollutants: Ambient Monitoring Data and Regional Air Quality Simulation

Cited by 3 publications

References 0 publications

Geocoding and spatio-temporal modeling of long-term PM2.5 and NO2 exposure: the Mexican Teachers' Cohort

Geocoding and spatio-temporal modeling of long-term PM2.5 and NO2 exposure: the Mexican Teachers' Cohort

Comparison of Prediction Methods for Air Pollution Data in Malaysia and Singapore

Long-term particulate matter modeling for health effects studies in California – Part 1: Model performance on temporal and spatial variations

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