National PM2.5 and NO2 Exposure Models for China Based on Land Use Regression, Satellite Measurements, and Universal Kriging

by   Hao Xu, et al.

Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in China. China is the most populous country in the world and also has the largest number of air pollution deaths per year, yet the spatial resolution of existing national air pollution estimates for China is generally relatively low. We address this knowledge gap by developing and evaluating national empirical models for China incorporating land-use regression (LUR), satellite measurements, and universal kriging (UK). We test the resulting models in several ways, including (1) comparing models developed using forward stepwise regression vs. partial least squares (PLS) regression, (2) comparing models developed with and without satellite measurements, and with and without UK, and (3) 10-fold cross-validation (CV), leave-one-province-out(LOPO) CV, and leave-one-city-out(LOCO) CV. Satellite data and kriging are complementary in making predictions more accurate: kriging improved the models in well-sampled areas; satellite data substantially improved performance at locations far away from monitors. Stepwise forward selection performs similarly to PLS in 10-fold CV, but better than PLS in LOPO-CV. Our best models employ forward selection and UK, with 10-fold CV R2 of 0.89 (for both 2014 and 2015) for PM2.5 and of 0.73 (year-2014) and 0.78 (year-2015) for NO2. Population-weighted concentrations during 2014-2015 decreased for PM2.5 (58.7 μg/m3 to 52.3 μg/m3) and NO2 (29.6 μg/m3 to 26.8 μg/m3). We produced the first high resolution national LUR models for annual-average concentrations in China. Models were applied on 1 km grid to support future research. In 2015, more than 80 in areas that exceed the Chinese national PM2.5 standard, 35 μg/m3. Results here will be publicly available and may be useful for environmental health research.



There are no comments yet.


page 18


A comparison of statistical and machine learning methods for creating national daily maps of ambient PM_2.5 concentration

A typical problem in air pollution epidemiology is exposure assessment f...

Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space

Air pollution is a major driver of climate change. Anthropogenic emissio...

Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide

Nitrogen dioxide (NO_2) is a primary constituent of traffic-related air ...

Coloring panchromatic nighttime satellite images: Elastic maps vs. kernel smoothing and multivariate regression approach

Artificial light-at-night (ALAN), emitted from the ground and visible fr...

Estimates of daily ground-level NO2 concentrations in China based on big data and machine learning approaches

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutan...

A Deep Learning Approach for Population Estimation from Satellite Imagery

Knowing where people live is a fundamental component of many decision ma...

Combining Parametric Land Surface Models with Machine Learning

A hybrid machine learning and process-based-modeling (PBM) approach is p...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.