Evidence of air quality data misreporting in China: An impulse indicator saturation model comparison of local government-reported and U.S. embassy-reported PM2.5 concentrations (2015–2017)

Abstract

This paper analyzes hourly PM2.5 measurements from government-controlled and U.S. embassy-controlled monitoring stations in five Chinese cities between January 2015 and June 2017. We compare the two datasets with an impulse indicator saturation technique that identifies hours when the relation between Chinese and U.S. reported data diverges in a statistically significant fashion. These temporary divergences, or impulses, are 1) More frequent than expected by random chance; 2) More positive than expected by random chance; and 3) More likely to occur during hours when air pollution concentrations are high. In other words, relative to U.S.-controlled monitoring stations, government-controlled stations systematically under-report pollution levels when local air quality is poor. These results contrast with the findings of other recent studies, which argue that Chinese air quality data misreporting ended after a series of policy reforms beginning in 2012. Our findings provide evidence that local government misreporting did not end after 2012, but instead continued in a different manner. These results suggest that Chinese air quality data, while still useful, should not be taken entirely at face value.

Public Diplomacy and Air Pollution Problem: How the U.S. Used “Beijing Air” to Advance Its Soft Power in China

“…This led some to wonder if the leaders were making the numbers up (Stoerk 2016;Lim 2013). The air quality problem in China has been politicized amid growing public discontent and the Beijing municipal government has systematically reported inaccurate pollution levels when the air quality in the region was poor (Turiel and Kaufmann 2021;Stoerk 2016). In particular, the fact that a day with an air quality index (AQI) of 100μg or less was designated as "Blue Sky Day," and the number of "Blue-Sky Days" was included in the performance evaluation and criteria for the promotion of local government officials, contributes to explaining why misinformation was prevalent (Stoerk 2016).…”

Impacts of Energy Efficiency and Economic Growth on Air Pollutant Emissions: Evidence from Angara–Yenisey Siberia

“…The misreporting of environmental data is a common phenomenon and is widely studied in China [35,55]. Despite there being no statistically grounded evidence of systematic distortions of the official Russian environmental data, we assumed that the available data may also be affected by various kinds of intentional or accidental errors [46].…”

“…It is worth mentioning that the quality of official data on air pollution is usually controversial [35][36][37][38], so we assume that all the figures from state sources are the lower estimates of the factual levels of corresponding indicators. Meanwhile, the data from nongovernmental sources such as IQ Air (https://www.iqair.com/ (accessed on 15 July 2021)) and similar independent air quality monitoring projects should also be used with care in respect to the unknown accuracy and credibility of primary measurements and probable overestimation of the results [39].…”

Blockchain-Based Network Concept Model for Reliable and Accessible Fine Dust Management System at Construction Sites

“…However, based on a centralized server, the current fine dust management system may result in information imbalance [12]. As an example, From January 2015 to June 2017, the amount of fine dust generated by China's five major cities (Beijing, Shanghai, Shenyang, Guangzhou, and Chengdu) was significantly lower than the value measured by the US embassy [13]. This proved that it is difficult to trust the information provided by the Chinese local government, which centrally manages information.…”

“…Moreover, accessing information related to fine dust monitoring systems is difficult for the public. Since the current monitoring system is a centralized system, there is no responsibility to share information on fine dust sensors at construction sites, and manipulation of the information provided to the public may occur [13,18]. Therefore, it is difficult to remotely read the fine dust measurements at construction sites and determine whether the contractor carried out the fine dust reduction measures.…”