Investigating the Transport Mechanism of PM2.5 Pollution during January 2014 in Wuhan, Central China

Severe haze pollution that occurred in January 2014 in Wuhan was investigated. The factors leading to Wuhan’s PM2.5 pollution and the characteristics and formation mechanism were found to be significantly different from other megacities, like Beijing. Both the growth rates and decline rates of PM2.5 concentrations in Wuhan were lower than those in Beijing, but the monthly PM2.5 value was approximately twice that in Beijing. Furthermore, the sharp increases of PM2.5 concentrations were often accompanied by strong winds. A high-precision modeling system with an online source-tagged method was established to explore the formation mechanism of five haze episodes. The long-range transport of the polluted air masses from the North China Plain (NCP) was the main factor leading to the sharp increases of PM2.5 concentrations in Wuhan, which contributed 53.4% of the monthly PM2.5 concentrations and 38.5% of polluted days. Furthermore, the change in meteorological conditions such as weakened winds and stable weather conditions led to the accumulation of air pollutants in Wuhan after the long-range transport. The contribution from Wuhan and surrounding cities to the PM2.5 concentrations was determined to be 67.4% during this period. Under the complex regional transport of pollutants from surrounding cities, the NCP, East China, and South China, the five episodes resulted in 30 haze days in Wuhan. The findings reveal important roles played by transregional and intercity transport in haze formation in Wuhan.

Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network

Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM_(10) than PM_(2.5) in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants(PM_(2.5), PM_(10),SO_2,NO_2,CO and O_3) from 1436 stations of the China National Environmental Monitoring Network during 2014-16. Among the measurements, 0.65%-5.68% are marked as outliers. with PM_(10) and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016,which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM_(2.5),with differences exceeding 10 μg m^(-3) at 66 sites.

Impacts of meteorology and precursor emission change on O_(3) variation in Tianjin, China from 2015 to 2021

Deterioration of surface ozone (O_(3)) pollution in Northern China over the past few years received much attention.For many cities,it is still under debate whether the trend of surface O_(3)variation is driven by meteorology or the change in precursors emissions.In this work,a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algorithm were utilized to quantify the meteorological impacts on the recorded O_(3)trend and identify the key meteorological factors affecting O_(3)pollution in Tianjin,the biggest coastal port city in Northern China.After “removing” the meteorological fluctuations from the observed O_(3)time series,we found that variation of O_(3)in Tianjin was largely driven by the changes in precursors emissions.The meteorology was unfavorable for O_(3)pollution in period of 2015-2016,and turned out to be favorable during 2017-2021.Specifically,meteorology contributed 9.3μg/m^(3)O_(3)(13%) in 2019,together with the increase in precursors emissions,making 2019 to be the worst year of O_(3)pollution since 2015.Since then,the favorable effects of meteorology on O_(3)pollution tended to be weaker.Temperature was the most important factor affecting O_(3)level,followed by air humidity in O_(3)pollution season.In the midday of summer days,O_(3)pollution frequently exceeded the standard level (>160μg/m^(3)) at a combined condition with relative humidity in 40%-50%and temperature>31℃.Both the temperature and the dryness of the atmosphere need to be subtly considered for summer O_(3)forecasting.