Real‐time PM<sub>2.5</sub> mapping and anomaly detection from AirBoxes in Taiwan

Huang, Guowen; Chen, Ling-Jyh; Hwang, Whie-Ann; Tzeng, Sheng Li; Huang, Hsin‐Cheng

doi:10.1002/env.2537

Cited by 13 publications

(8 citation statements)

References 33 publications

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“…where f 0 (•) is an unknown function of the intercept and f 1 (•) is an unknown function of the slope. Following Huang et al (2018), we model the hidden Gaussian process y t (•) corresponding to AirBoxes as:…”

Section: Motivated By the Preliminary Calibration Results At Epa Loca...mentioning

confidence: 99%

“…As demonstrated in Huang et al (2018), the spatial prediction is not much affected by K, since both the basis functions ϕ(•) and the spatial process η t (•) compete to capture y t (•).…”

Section: Spatial Prediction Based On Calibrated Airbox Datamentioning

confidence: 96%

“…Similar to Huang et al (2018), we select K = 25 so that the function in the projected space accounts for about 50% of the variation in the AirBox data. Alternatively, K can be selected by using the conditional Akaike's information criterion of Vaida and Blanchard (2005) randomly divided the EPA data (on the main island with 74 stations) into the training data z * t (consisting of 2/3 of non-missing observations with various sizes) and set the remaining data z * * t ≡ z * * t (s * * t1 ), .…”

Section: Spatial Prediction Based On Calibrated Airbox Datamentioning

confidence: 99%

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Spatially Adaptive Calibrations of AirBox PM$_{2.5}$ Data

Tzeng,

Lai,

Huang

2021

Preprint

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Two networks are available to monitor PM 2.5 in Taiwan, including the Taiwan Air Quality Monitoring Network (TAQMN) and the AirBox network. The TAQMN, managed by Taiwan's Environmental Protection Administration (EPA), provides high-quality PM 2.5 measurements at 77 monitoring stations. More recently, the AirBox network was launched, consisting of low-cost, small internet-of-things (IoT) microsensors (i.e., AirBoxes) at thousands of locations. While the AirBox network provides broad spatial coverage, its measurements are not reliable and require calibrations. However, applying a universal calibration procedure to all AirBoxes does not work well because the calibration curves vary with several factors, including the chemical compositions of PM 2.5 , which are not homogeneous in space. Therefore, different calibrations are needed at different locations with different local environments. Unfortunately, most AirBoxes are not close to EPA stations, making the calibration task challenging. In this article, we propose a spatial model with spatially varying coefficients to account for heteroscedasticity in the data. Our method gives adaptive calibrations of AirBoxes according to their local conditions and provides accurate PM 2.5 concentrations at any location in Taiwan, incorporating two types of measurements. In addition, the proposed method automatically calibrates measurements from a new AirBox once it is added to the network. We illustrate our approach using hourly PM 2.5 data in the year 2020. After the calibration, the results show that the PM 2.5 prediction improves about 37% to 67% in root mean-squared prediction error for matching EPA data. In particular, once the calibration curves are established, we can obtain reliable PM 2.5 values at any location in Taiwan, even if we ignore EPA data.

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Section: Motivated By the Preliminary Calibration Results At Epa Loca...mentioning

confidence: 99%

Section: Spatial Prediction Based On Calibrated Airbox Datamentioning

confidence: 96%

Section: Spatial Prediction Based On Calibrated Airbox Datamentioning

confidence: 99%

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Spatially Adaptive Calibrations of AirBox PM$_{2.5}$ Data

Tzeng,

Lai,

Huang

2021

Preprint

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“…Our proposed method builds on a substantial body of research on statistical methods to measure or estimate exposure to PM 2.5 , PM components, other environmental pollutants. This includes methods to infer exposures from existing monitoring networks, deployment of networks of portable devices, smartphones, and personal monitors (Calder, 2008; Das & Ghosal, 2017; Finazzi & Paci, 2019; Huang, Chen, Hwang, Tzeng, & Huang, 2018; Rundel, Schliep, Gelfand, & Holland, 2015).…”

Section: Discussionmentioning

confidence: 99%

Bayesian nonparametric monotone regression

et al. 2020

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In many applications there is interest in estimating the relation between a predictor and an outcome when the relation is known to be monotone or otherwise constrained due to the physical processes involved. We consider one such application-inferring time-resolved aerosol concentration from a low-cost differential pressure sensor. The objective is to estimate a monotone function and make inference on the scaled first derivative of the function. We proposed Bayesian nonparametric monotone regression, which uses a Bernstein polynomial basis to construct the regression function and puts a Dirichlet process prior on the regression coefficients. The base measure of the Dirichlet process is a finite mixture of a mass point at zero and a truncated normal. This construction imposes monotonicity while clustering the basis functions. Clustering the basis functions reduces the parameter space and allows the estimated regression function to be linear. With the proposed approach we can make closed-formed inference on the derivative of the estimated function including full quantification of uncertainty. In a simulation study the proposed method performs similar to other monotone regression approaches when the true function is wavy but performs better when the true function is linear. We apply the method to estimate time-resolved aerosol concentration with a newly developed portable aerosol monitor. The R package bnmr is made available to implement the method.

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“…Airborne pollution and other environmental processes are commonly elaborated by geostatistical methods, like hierarchical models (e.g., Finazzi, Scott, and Fassò ; Fassò ; Fassò, Finazzi, and Ndongo ; Huang et al. ; Mastrantonio et al. ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Anisotropic, Time‐Varying Spatial Spillovers of Fine Particulate Matter Due to Wind Direction

Merk

Otto

2019

Geographical Analysis

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This paper investigates the effect of daily wind direction and speed on the spatio-temporal distribution of particulate matter, PM 2.5 . Interdependencies between the PM 2.5 values of different monitoring sites are characterized by incorporating time-varying anisotropic spatial weighting matrices. These weights are parameterized with respect to wind direction, speed and a range that marks the bandwidth of admissible deviations between wind direction and bearing. The empirical analysis is based on daily PM 2.5 values recorded by monitoring sites located across the eastern United States in 2015 as well as several meteorological regressors. More precisely, we propose a space-time dynamic panel data model with different spatial autoregressive, temporal and exogenous dependencies. All model parameters are estimated by the quasi-maximum likelihood approach. The estimation procedure, including the identification of the range and spatial parameters, is verified by Monte Carlo simulations. We show that part of the spatial dependency of PM 2.5 values is explained by wind direction.Estimation of Anisotropic, Time-Varying Spatial Spillovers 255 spillover effects of PM 2.5 (particulate matter with a diameter less than or equal to 2.5 μm). Wind directions induce spatial dependencies that are not uniform in all directions, so the time-varying spatial weights are also considered to be anisotropic. While the influence of meteorological regressors, which contribute considerably to the distribution and even deposition of PM 2.5 , has been extensively investigated (e.g., DeGaetano and Doherty 2004;Fassò 2013;Tai, Mickley, and Jacob 2010), fewer studies have considered the effect of wind direction. Tai, Mickley, and Jacob (2010) investigated which wind directions were strongly associated with high concentrations of major PM 2.5 components. Sanchez-Reyna et al. (2005) also found that particular wind directions were frequently accompanied by PM 10 increases in London. Similarly, Guerra et al. (2006) reported that wind directions from major industrial areas led to higher PM 2.5 and PM 10 concentrations in Kansas. However, in other respects, time-varying anisotropic spatially autoregressive dependencies due to wind direction have been widely ignored when analyzing or interpolating distributions of particulate matter. We therefore investigate the contribution of these dependencies by employing time-varying anisotropic spatial weighting matrices.The specification of a weighting matrix that reflects the spatial structure and interrelations of cross-sectional units of a sample is one of the key issues in spatial regression models. When working with georeferenced data, those weights are typically predetermined by geographical characteristics such as common borders, nearest-neighbour dependencies or distance decays. In these cases, the typical assumption of non-stochastic weights is not violated. In addition, those characteristics are often permanent or at least slowly evolving over time such that it has become conclusive to draw on constant ...

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Real‐time PM_2.5 mapping and anomaly detection from AirBoxes in Taiwan

Cited by 13 publications

References 33 publications

Spatially Adaptive Calibrations of AirBox PM$_{2.5}$ Data

Spatially Adaptive Calibrations of AirBox PM$_{2.5}$ Data

Bayesian nonparametric monotone regression

Estimation of Anisotropic, Time‐Varying Spatial Spillovers of Fine Particulate Matter Due to Wind Direction

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Real‐time PM2.5 mapping and anomaly detection from AirBoxes in Taiwan

Cited by 13 publications

References 33 publications

Spatially Adaptive Calibrations of AirBox PM$_{2.5}$ Data

Spatially Adaptive Calibrations of AirBox PM$_{2.5}$ Data

Bayesian nonparametric monotone regression

Estimation of Anisotropic, Time‐Varying Spatial Spillovers of Fine Particulate Matter Due to Wind Direction

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Product

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Real‐time PM_2.5 mapping and anomaly detection from AirBoxes in Taiwan