Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation

Dharmalingam, Selvaraj; Natesan, Senthilkumar; D’Souza, Rohan; Hu, Yongtao; Chang, Howard H.; Ebelt, Stefanie; Yu, Haofei; Kim, Chloe S.; Rohr, Annette

doi:10.1016/j.envres.2021.112207

Cited by 8 publications

(4 citation statements)

References 64 publications

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“…For example, R 2 values are expected to be higher at locations with a wider range of concentrations. Including more metrics, such as mean absolute error or mean fractional error, would be more indicative of model performances. , We nonetheless still choose to use R 2 because it is suitable for quantifying how much temporal pollution concentration variability is captured by the model. Fourth, we only used uncorrected voltage data collected from working and reference electrodes in this study as we do not have voltage data from other electrodes (e.g., auxiliary) readily available.…”

Section: Resultsmentioning

confidence: 99%

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Hasan

Ivey

et al. 2023

ACS Omega

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Low-cost air quality (LCAQ) sensors are increasingly being used for community air quality monitoring. However, data collected by low-cost sensors contain significant noise, and proper calibration of these sensors remains a widely discussed, but not yet fully addressed, area of concern. In this study, several LCAQ sensors measuring nitrogen dioxide (NO 2 ) and ozone (O 3 ) were deployed in six cities in the United States

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Section: Resultsmentioning

confidence: 99%

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Hasan

Ivey

et al. 2023

ACS Omega

Self Cite

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“…Existen diferentes estudios que han utilizado las técnicas de interpolación espacial tales como Ponderación de Distancia Inversa (Inverse Distance Weighted, IDW, por sus siglas en inglés) y Kriging para obtener la variabilidad espacial de contaminantes ambientales, como el SO2 [17,18,19,20,21]. Por ejemplo, [17] utilizaron IDW y Kriging para estimar la distribución espacial de SO2 y otros contaminantes ambientales por el uso de combustibles fósiles en Chengdu, China.…”

Section: Introductionunclassified

“…En el estudio por [20] realizaron una interpolación espacial de SO2, dióxido de nitrógeno y material particulado en la ciudad de Mumbai, India usando IDW y Kriging. Recientemente, [21] implementaron IDW y Kriging para estimar la exposición de diferentes contaminantes ambientales en Atlanta, Georgia usando datos recolectados durante cinco años. Entre los estudios existentes, no se tiene conocimiento de ninguno que haya implementado ambos métodos de interpolación espacial del contaminante SO2 en la Bahía de Quintero, como en el presente estudio.…”

Section: Introductionunclassified

Prediction of the Ambient Air Pollution due to SO2 in the Bay of Quintero, Chile

Collao¹,

Blázquez²

2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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Ambient air pollution causes serious health problems and premature death in people. Spatial interpolation methods have been widely used to estimate the exposure of contaminants. In this study, spatial interpolation methods (inverse distance weighted, IDW and Ordinary Kriging) were employed to predict SO2 concentrations in the Bay of Quintero, Chile. Overall, the results suggest that there is uniform spatial distribution for low SO2 concentrations and an evident spatial variability for high SO2 concentrations. Spatial differences exist for high values of SO2 measurements, indicating that the south-east zone of the Bay of Quintero has a larger exposure of the SO2 contaminant. When comparing interpolation methods, Ordinary Kriging present better performance than IDW. The results of this study may help support authorities and policymakers in the implementation of environmental actions to reduce ambient air pollution in the studied zone.

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“…Some studies have compared empirical models with mechanistic models (e.g., CMAQ) (Marshall et al, 2008;Samoli et al, 2020), satellite-based models (e.g., aerosol optical depth, AOD) (Yu et al, 2018;Cowie et al, 2019), or hybrid models (Michanowicz et al, 2016;Zhang et al, 2021). Other studies have compared results using different methods for model-building (e.g., LUR vs. machine learning vs. kriging vs. hybrid empirical models) (Adam-Poupart et al, 2014;Jain et al, 2021;Dharmalingam et al, 2022). However, most prior comparisons were at the city or region level, and comparisons were generally within a single research team.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of six national empirical models for PM2.5 air pollution in the contiguous US

Bechle

Bell

Goldberg

et al. 2022

Preprint

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Empirical models, previously called land-use regression (LUR), are used to understand and predict spatial variability in levels of outdoor air pollution at unmeasured locations, for example, to conduct health risk assessment, environmental epidemiology, or environmental justice analysis. Many methods are used to generate empirical models, yet almost no research compares models generated by separate research groups. We intercompare six national-scale empirical models for year-2010 concentrations of PM2.5 in the US, each generated by a different research group. Despite substantial differences in the statistical methods and input data used to build the models, our main finding is a relatively high degree of agreement among model predictions. For example, in pairwise intercomparisons, the average Pearson correlation coefficient is 0.87 (range: 0.84 to 0.92); the RMSD (root-mean-square-difference; units: μg/m3) is 1.1 on average (range: 0.8 to 1.4), or ~12% of the average concentration; and many best-fit lines are near the 1:1 line. The underlying reason for this agreement is likely that, while the methods and the independent variables differ among the models, in all cases the models are built using, and are calibrated to, the same information: publicly available measurement at US EPA regulatory monitoring stations. Findings here suggest that future improvements to national empirical models will come not from further refinements to the methods (e.g., more-advanced models) but from employing a fundamentally different set of observations, in addition to regulatory monitoring data.

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Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation

Cited by 8 publications

References 64 publications

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Prediction of the Ambient Air Pollution due to SO2 in the Bay of Quintero, Chile

Intercomparison of six national empirical models for PM2.5 air pollution in the contiguous US

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