Validation of OMI HCHO Products Using MAX-DOAS observations from 2010 to 2016 in Xianghe, Beijing: Investigation of the Effects of Aerosols on Satellite Products

The association between long-term exposure to ambient formaldehyde and respiratory mortality risk: A national study in China

INTRODUCTION

While ambient formaldehyde (HCHO) concentrations are increasing worldwide, there was limited research on its health effects.

OBJECTIVES

To assess the association of long-term exposure to ambient HCHO with the risk of respiratory (RESP) mortality and the associated mortality burden in China.

METHODS

Annual and seasonal RESP death and tropospheric HCHO vertical columns data were collected in 466 counties/districts across China during 2013-2016. A difference-in-differences approach combined with a generalized linear mixed-effects regression model was employed to assess the exposure-response association between long-term ambient HCHO exposure and RESP mortality risk. Additionally, we computed the attributable fraction (AF) to gauge the proportion of RESP mortality attributable to HCHO exposure.

RESULTS

This analysis encompassed 560,929 RESP deaths. The annual mean ambient HCHO concentration across selected counties/districts was 8.02×1015 ± 2.22×1015 molec.cm-2 during 2013-2016. Each 1.00×1015 molec.cm-2 increase in ambient HCHO was associated with a 1.61 % increase [excess risk (ER), 95 % confidence interval (CI): 1.20 %, 2.03 %] in the RESP mortality risk. The AF of RESP mortality attributable to HCHO was 12.16 % (95 %CI:9.33 %, 14.88 %), resulting in an annual average of 125,422 (95 %CI:96,404, 153,410) attributable deaths in China. Stratified analyses suggested stronger associations in individuals aged ≥65 years old (ER=1.87 %, 95 %CI:1.43 %, 2.32 %), in cold seasons (ER=1.00 %, 95 %CI:0.56 %, 1.44 %), in urban areas (ER=1.65 %, 95 %CI:1.15 %, 2.16 %), and in chronic obstructive pulmonary disease patients (ER=1.95 %, 95 %CI:1.42 %, 2.48 %).

CONCLUSIONS

This study suggested that long-term HCHO exposure may significantly increase the risk of RESP mortality, leading to a substantial mortality burden. Targeted measures should be implemented to control ambient HCHO pollution promptly.

Regional Atmospheric Aerosol Pollution Detection Based on LiDAR Remote Sensing

Atmospheric aerosol is one of the major factors that cause environmental pollution. Light detection and ranging (LiDAR) is an effective remote sensing tool for aerosol observation. In order to provide a comprehensive understanding of the aerosol pollution from the physical perspective, this study investigated regional atmospheric aerosol pollution through the integration of measurements, including LiDAR, satellite, and ground station observations and combined the backward trajectory tracking model. First, the horizontal distribution of atmospheric aerosol wa obtained by a whole-day working scanning micro-pulse LiDAR placed on a residential building roof. Another micro-pulse LiDAR was arranged at a distance from the scanning LiDAR to provide the vertical distribution information of aerosol. A new method combining the slope and Fernald methods was then proposed for the retrieval of the horizontal aerosol extinction coefficient. Finally, whole-day data, including the LiDAR data, the satellite remote sensing data, meteorological data, and backward trajectory tracking model, were selected to reveal the vertical and horizontal distribution characteristics of aerosol pollution and to provide some evidence of the potential pollution sources in the regional area. Results showed that the aerosol pollutants in the district on this specific day were mainly produced locally and distributed below 2.0 km. Six areas with high aerosol concentration were detected in the scanning area, showing that the aerosol pollution was mainly obtained from local life, transportation, and industrial activities. Correlation analysis with the particulate matter data of the ground air quality national control station verified the accuracy of the LiDAR detection results and revealed the effectiveness of LiDAR detection of atmospheric aerosol pollution.