A homogenized daily in situ PM<sub>2.5</sub> concentration dataset from the national air quality monitoring network in China

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.

Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion

“…Among them, ground measurements are the most accurate way to obtain ambient PM 2.5 concentrations. However, due to the installation and maintenance costs of ground networks, monitoring stations are usually sparse and unevenly distributed, with most of the stations located in urban areas. , Moreover, PM 2.5 ground networks in China were established in 2013, so prior data are unavailable. As an alternative, chemical transport models (CTMs) could provide complete-coverage simulations of PM 2.5 concentrations and could reproduce the spatial and temporal trends of PM 2.5 concentrations when using reasonable emission inventories. , However, biases still exist in the simulated absolute values of PM 2.5 due to uncertainties in emission inventories and the lack of certain physical and chemical processes in the model. − Satellite-retrieved aerosol optical depth (AOD) data, which can reflect the aerosol abundance in the atmosphere, have the advantage of long-term records and a high resolution.…”

“…However, due to the installation and maintenance costs of ground networks, monitoring stations are usually sparse and unevenly distributed, with most of the stations located in urban areas. 11,12 Moreover, PM 2.5 ground networks in China were established in 2013, so prior data are unavailable. As an alternative, chemical transport models (CTMs) could provide complete-coverage simulations of PM 2.5 concentrations and could reproduce the spatial and temporal trends of PM 2.5 concentrations when using reasonable emission inventories.…”

Reconstruction of daily haze data across China between 1961 and 2020

“…Studies have shown that the anthropogenic PM 10 and PM 2.5 emissions in China decreased by 38 and 35% in 2017 compared to 2010 PM levels, respectively; these reductions were mainly caused by the implementation of the Air Pollution Prevention and Control Action Plan in 2013 (Zheng et al ., 2018). Based on the homogenized data from the National Air Quality Monitoring Network of China, PM 2.5 concentrations across China decreased by 7.3% per year between 2015 and 2019 (Bai et al ., 2020). Similarly, PM 2.5 concentrations estimated from MODIS aerosol optical depth (AOD) data also revealed a downward trend in particulate concentrations between 2012 and 2017 in city clusters such as BTH, Yangtze River Delta (YRD; which includes Shanghai, Jiangsu, Zhejiang, and Anhui provinces), and Pearl River Delta (PRD; which includes Guangdong Province) (Xue et al ., 2020).…”

LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion

“…The gauged air pollutant concentration data have been released online on an hourly basis by the China National Environmental Monitoring Centre since late 2013. For quality control, outliers were first detected and removed from each pollutant dataset by following the criteria used in our previous study (Bai et al, 2020a). The missing values were then reconstructed using the diurnal-cycle-constrained empirical orthogonal function (DCCEOF) method proposed in Bai et al (2020b).…”