A homogenized daily in situ PM<sub>2.5</sub> concentration dataset from the national air quality monitoring network in China
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Published:2020-11-25
Issue:4
Volume:12
Page:3067-3080
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ISSN:1866-3516
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Container-title:Earth System Science Data
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language:en
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Short-container-title:Earth Syst. Sci. Data
Author:
Bai Kaixu, Li Ke, Wu Chengbo, Chang Ni-Bin, Guo JianpingORCID
Abstract
Abstract. In situ PM2.5 concentration observations have long been used as critical data
sources in haze-related studies. Due to the frequently occurring haze
pollution events, China started to regularly monitor PM2.5
concentration nationwide from the newly established air quality monitoring
network in 2013. Nevertheless, the acquisition of these invaluable air
quality samples is challenging given the absence of a publicly available data
download interface. In this study, we provided a homogenized in situ PM2.5
concentration dataset that was created on the basis of hourly PM2.5
data retrieved from the China National Environmental Monitoring Center
(CNEMC) via a web crawler between 2015 and 2019. Methods involving missing
value imputation, change point detection, and bias adjustment were applied
sequentially to deal with data gaps and inhomogeneities in raw PM2.5
observations. After excluding records with limited samples, a homogenized
PM2.5 concentration dataset comprising of 1309 5-year long
PM2.5 data series at a daily resolution was eventually compiled. This
is the first attempt to homogenize in situ PM2.5 observations in China. The
trend estimations derived from the homogenized dataset indicate a spatially
homogeneous decreasing tendency of PM2.5 across China at a mean rate of
about −7.6 % per year from 2015 to 2019. In contrast to raw PM2.5
observations, the homogenized data record not only has complete data
integrity but is more consistent over space and time. This homogenized daily
in situ PM2.5 concentration dataset is publicly accessible at
https://doi.org/10.1594/PANGAEA.917557 (Bai et al., 2020a) and can be
applied as a promising dataset for PM2.5-related studies such as
satellite-based PM2.5 mapping, human exposure risk assessment, and air
quality management.
Funder
National Natural Science Foundation of China Ministry of Science and Technology of the People's Republic of China
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
Reference51 articles.
1. Bai, K., Chang, N.-B., Yu, H., and Gao, W.: Statistical bias correction for creating coherent total ozone record from OMI and OMPS observations, Remote Sens. Environ., 182, 150–168, https://doi.org/10.1016/j.rse.2016.05.007, 2016. 2. Bai, K., Chang, N.-B., Zhou, J., Gao, W., and Guo, J.: Diagnosing
atmospheric stability effects on the modeling accuracy of PM2.5/AOD
relationship in eastern China using radiosonde data, Environ. Pollut., 251,
380–389, https://doi.org/10.1016/j.envpol.2019.04.104, 2019a. 3. Bai, K., Li, K., Chang, N.-B., and Gao, W.: Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: A perspective of data mining through in situ PM2.5 measurements, Environ. Pollut., 254, 113047, https://doi.org/10.1016/j.envpol.2019.113047, 2019b. 4. Bai, K., Ma, M., Chang, N.-B., and Gao, W.: Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data, J. Environ. Manage., 233, 530–542, https://doi.org/10.1016/j.jenvman.2018.12.071, 2019c. 5. Bai, K., Li, K., Wu, C., Chang, N.-B., and Guo, J.: A homogenized daily in situ
PM2.5 concentration dataset in China during 2015–2019, PANGAEA,
https://doi.org/10.1594/PANGAEA.917557, 2020a.
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