Satellite-Based Estimates of Daily NO2 Exposure in China Using

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Environmental Modeling

Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model Yu Zhan, Yuzhou Luo, Xunfei Deng, Kaishan Zhang, Minghua Zhang, Michael L. Grieneisen, and Baofeng Di Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05669 • Publication Date (Web): 16 Mar 2018 Downloaded from http://pubs.acs.org on March 16, 2018

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Environmental Science & Technology

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Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest

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and Spatiotemporal Kriging Model

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Yu Zhan,†,‡,§ Yuzhou Luo,‖ Xunfei Deng,⊥ Kaishan Zhang,† Minghua Zhang,‖

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Michael L. Grieneisen,‖ Baofeng Di*,‡,†

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†

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610065, China

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‡

Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan

Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, Sichuan

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610200, China

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§

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610065, China

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Sino-German Centre for Water and Health Research, Sichuan University, Chengdu, Sichuan

‖

Department of Land, Air, and Water Resources, University of California, Davis, CA 95616,

USA ⊥

Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou,

Zhejiang 310021, China

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*

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Tel: +86 13982079978; fax +86 2885405613; e-mail: [email protected]

Corresponding author

1 ACS Paragon Plus Environment


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ABSTRACT

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A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to

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estimate the daily ambient NO2 concentrations across China during 2013-2016 based on the

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satellite retrievals and geographic covariates. The RF-STK model showed good prediction

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performance, with cross-validation R2=0.62 (RMSE=13.3 µg/m3) for daily and R2=0.73

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(RMSE=6.5 µg/m3) for spatial predictions. The nationwide population-weighted multiyear

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average of NO2 was predicted to be 30.9±11.7 µg/m3 (mean±standard deviation), with a slowly

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but significantly decreasing trend at a rate of -0.88±0.38 µg/m3/year. Among the main economic

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zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 µg/m3/year,

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while the Beijing-Tianjin Metro did not show a temporal trend (P=0.32). The population-

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weighted NO2 was predicted to be the highest in North China (40.3±10.3 µg/m3) and lowest in

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Southwest China (24.9±9.4 µg/m3). Approximately 25% of the population lived in nonattainment

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areas with annual-average NO2>40 µg/m3. A piecewise linear function with an abrupt point

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around 100 people/km2 characterized the relationship between the population density and the

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NO2, indicating a threshold of aggravated NO2 pollution due to urbanization. Leveraging the

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ground-level NO2 observations, this study fills the gap of statistically modeling nationwide NO2

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in China, and provides essential data for epidemiological research and air quality management.


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TOC Art

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INTRODUCTION

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Ambient nitrogen dioxide (NO2) causes direct damage to human health and contributes to the

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formation of ozone (O3), particulate matter (PM), and acid rain.1, 2 Exposure to NO2 has been

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associated with adverse human health outcomes, such as asthma, respiratory disorders, lung

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cancer, and premature mortality.2, 3 As a valuable marker of air pollution mixtures, NO2 is an

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important precursor of O3 and PM, and their adverse effects on human health may be mutually

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reinforcing.2, 4 Atmospheric NO2 is mainly emitted from anthropogenic sources, such as motor

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vehicle emissions and industrial boilers.1 In China, as one of the highly NO2 polluted countries in

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the world,5 the number of private cars has increased by more than 500% in the past decade.6

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More than a thousand state-managed sites have been established in China to monitor the

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concentrations of ambient NO2 and other air pollutants since 2013. High uncertainty emerges

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when assessing the exposure levels through site-matching, especially for the areas distant from

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the nearest monitoring site. It is therefore critical to accurately predict the complete

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spatiotemporal distribution of NO2 across the entire country for population exposure assessment.

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Land use regression (LUR) models are commonly employed to predict spatial distributions of

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NO2 worldwide.7-10 In a narrow sense, land uses surrounding monitoring sites (as predictors) and

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observed NO2 are used to parameterize linear regression (LR) models, which predict NO2 at

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unmonitored locations based on the data from the predictors.11 In a broad sense, predictors of

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LUR models include many other geographic factors, such as population densities and

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meteorological conditions.12, 13 The satellite-retrieved vertical column density (VCD) of NO2 in

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the atmosphere is a particularly informative predictor for estimating NO2.7, 14 For instance, the

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ozone monitoring instrument (OMI) onboard the Aura satellite provides tropospheric NO2


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density with global coverage on a daily basis. A side-effect of including many predictors in an

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LUR model, e.g., ≥800 predictors were included in a previous LUR model,7 is the problem of

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severe multicollinearity among predictors, resulting in unreliable parameter estimation and lower

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prediction accuracy. Various techniques, such as stepwise variable selection, partial least square

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regression (PLSR), or least absolute shrinkage and selection operator (LASSO), are employed to

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resolve this multicollinearity problem.7, 10, 15 Nevertheless, LR is still inadequate for modeling

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NO2 given the complex relationships between the predictors and NO2, including nonlinearity and

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high-order interactions.

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Compared to LR models commonly used in LUR, machine learning models (e.g., random forests

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and neural networks) generally show higher prediction accuracy due to their strength in

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modeling complex relationships between response and predictor variables.16, 17 By investigating

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patterns from large amounts of data, machine learning algorithms develop sophisticated model

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structures for capturing relationships that are otherwise too complex to specify in parametric

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models such as LR. While machine learning models are usually considered black boxes, several

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statistical metrics are available for model interpretation, such as variable importance measures

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and partial dependence plots for random forests.18 For prediction-oriented tasks with primary

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concerns of prediction accuracy, machine learning models are generally more suitable than LR

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models when training data are abundant. Machine learning models have shown high performance

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in predicting ambient concentrations of multiple air pollutants, such as fine particulate matter

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(PM2.5) and O3.19-22 With the availability of the national air quality monitoring network in China

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and the millions of data points collected,23 machine-learning-based LUR models are more



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feasible than linear-regression-based LUR models for predicting the spatiotemporal distributions

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of NO2 for China.

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Most existing modeling studies on NO2 are focused on the prediction of spatial distributions, and

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only a few studies considered the intra-annual variation of NO2.13, 14, 24 A previous LUR study

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used temporal scaling to derive monthly NO2 from annual predictions based on monthly patterns

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observed at monitoring sites.14 This “top-down” scaling approach is satisfactory on a monthly

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scale but inadequate in predicting daily NO2, due to the fact that daily variation is much more

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irregular than monthly variation (Figure S1). To predict temporally resolved NO2, the temporal

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variation of predictors should be accounted for in LUR models.12, 25 A few previous studies used

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the predictors with temporal variation (e.g., satellite OMI retrievals and/or meteorological

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conditions) to predict daily or monthly NO2 by using linear-regression-based LUR models.12, 25

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However, similar to spatial LUR, the predictive performance of spatiotemporal LUR can be

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improved by replacing LR models with machine learning models. In addition, while national or

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continental-scale LUR modeling work for NO2 has been conducted for western countries, only a

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few local or regional-scale NO2-LUR studies exist for China.8, 24

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This study aims to fill that gap by estimating the spatiotemporal distributions of daily NO2 across

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China (0.1°×0.1° grid; 98341 cells) to facilitate nationwide exposure assessment. The spatial

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resolution of 0.1° is consistent with our previous work estimating spatiotemporal distributions of

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ambient ozone across China,22 and this resolution is commonly used in global or national

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exposure assessments.5, 26 A novel hybrid model of a random forest submodel and spatiotemporal

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kriging (RF-STK) is proposed to predict the daily NO2 concentrations based on the OMI satellite


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retrievals and various geographic covariates, as well as the spatiotemporal autocorrelations.

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Variable importance and partial dependence plots were employed to evaluate the effect of each

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predictor on the NO2 prediction. On the basis of the predicted NO2, we assessed the

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spatiotemporal patterns of NO2and the resulting population exposure levels. Moreover, the

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relationship between the population density and the NO2 was characterized with a piecewise

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linear function. Filling the gap of statistically modeling nationwide NO2 for China, this study

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provides essential data for epidemiological analyses and air quality management.

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MATERIALS AND METHODS

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Ground-level NO2 Observations. The hourly NO2 measurements were collected from 1657

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monitoring sites scattered throughout mainland China, Taiwan, and Hong Kong during 2013-

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2016 (Figure S2).23, 27, 28 The NO2 concentrations were measured using the chemiluminescence

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method. The number of monitoring sites increased from 744 to 1604 during 2013-2016. The

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hourly concentrations were averaged by days for each site, and days with less than 12-hour

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measurements were excluded. Strong diurnal patterns in NO2 were observed, with two peaks at

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8am and 21pm (Figure S3). While sampling bias towards urban areas was considerable,

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approximately 8% of the NO2 data were retrieved from areas with population densities lower

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than 400 people/km2 (Figure S4). These sites provided important training samples for modeling

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NO2 in suburban or rural areas. The units of NO2 were made uniform as µg/m3 by using the unit

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conversion factor (1 ppb = 1.88 µg/m3 NO2) in order to be consistent with the air quality

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guidelines set by WHO and the Chinese government.2 Approximately 1.67 million daily NO2

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observations were included for the model development. The number of observations per site was



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1008±344 (mean ± standard deviation), with negligible seasonal trend in the missing data (Figure

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S5).

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During 2013-2016 in China, the daily NO2 observations were 34±21 µg/m3, with a median of 29

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µg/m3 and an interquartile range of 26 µg/m3. The annual averages of the observed NO2

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decreased from 40±25 µg/m3 to 32±20 µg/m3 during 2013-2016. Seasonally, the NO2

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observations were the highest in winter (42±25 µg/m3) and the lowest in summer (25±14 µg/m3),

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with similar levels between spring (33±20 µg/m3) and fall (35±21 µg/m3). At the regional level,

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the highest and lowest mean NO2 levels were observed in North China (40±25 µg/m3) and South

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China (28±18 µg/m3), respectively.

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Satellite Retrievals. The tropospheric column densities of NO2 (molecules/cm2) were obtained

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from the OMI-NO2 level-3 data product (OMNO2d version 3; 0.25°×0.25° resolution).29 The

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OMI satellite retrievals had nearly global coverage on a daily basis, with a local bypass time

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during 12:00-15:00pm. The data of the level-3 product were regularly latitude-longitude gridded

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by calculating area-weighted means of all good-quality retrievals within each grid cell. The main

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quality screening criteria included terrain reflectivity

Satellite-Based Estimates of Daily NO2 Exposure in China Using

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