[Characteristics of BTEX and Health Risk Assessment During Typical Pollution Episodes in Summer and Winter in Tianjin Urban Area].

Real-time BTEX(including benzene, toluene, ethylbenzene, m-, p-, and o-xylenes) were measured continuously in Tianjin urban site in July 2019 and January 2020 using a Syntech Spectras GC955 analyzer. The BTEX concentration levels, composition, and evolutionary mechanisms during typical pollution episodes were investigated. The potential sources of BTEX were analyzed qualitatively using the diagnostic ratios method. Finally, the BTEX health risk was evaluated by using the human exposure analysis and evaluation method according to US EPA. The averaged total mixing ratio of BTEX were 1.32×10-9 and 4.83×10-9 during ozone pollution and haze episodes, respectively. Benzene was the most abundant species, followed by toluene. The mixing ratio of BTEX was largely affected by short southwestern distance transportation in January, while local emissions in July. In addition, the BTEX mixing ratio depended on the influence of temperature and relative humidity(RH) in July, while the concentration was more sensitive to changes in RH when the temperature was low in January. Diagnostic ratios and source implications suggested that the BTEX was affected mainly by biomass/biofuel/coal burning during haze episodes. The traffic related emissions also had an impact except for the influence of biomass/biofuel/coal burning in July. The averaged hazard quotient(HQ) values were 0.072 and 0.29 during ozone pollution and haze episodes, respectively, which were in the upper safety range limit recommended by the US EPA. The carcinogenic risk posed by benzene in both cleaning and pollution processes was higher than the safety threshold set by the US EPA, which should be monitored carefully.

[Similarities and Differences of Valley Winds in the Beijing Plain and Yanqing Areas and Its Impact on Pollution].

Under certain terrain and weather conditions, mountain-valley circulation is one of the main meteorological factors affecting aerosol pollution in plain-mountain area. Based on environmental monitoring data and multi-source meteorological data for the Beijing-Tianjin-Hebei region between 2015 and 2019, the characteristics, similarities, and differences of mountain-valley winds in the Beijing Plain and Yanhuai Basin regions were compared. The results show that the mountain-valley winds recorded at the Beijing Observatory are from southwest to northeast compared to from the southeast to northeast at Yanqing station. With the aggravation of pollution levels, the mountain-valley wind intensity decreased by 17.7%-32.4%. When the wind speed at Beijing Observatory was 2-6 m·s-1, the maximum PM2.5 concentration in southeast was 83 µg·m-3, which was higher than in the southwest. When the wind speed at the Yanqing station was 2-6 m·s-1, the PM2.5 concentrations in SE-SSE area was 20-40 µg·m-3 higher than in other directions, and the concentrations in the valley winds were 10-12 µg·m-3 higher than the average value for the last five years. Taking the typical heavy pollution event on March 5-8, 2015, as an example, the influence of mountain-valley winds is mainly reflected in the high humidity and regional transmission of southeast winds during the valley wind stage. The PM2.5 concentrations at the Yanqing station increased by 100-130 µg·m-3 during the valley wind stage on March 6 and 7, 2015. The inversion temperature developed to 1000 m during the mountain wind stage, the local dew point at the Beijing Observatory and the Yanqing station rose by approximately 18℃. The peak dew point at the Yanqing station occurring 2 hours after the Beijing Observatory, and the concentrations of PM2.5 rose slightly under high humidity conditions. Meanwhile, the thermal gradient between the 400-m-high Yanqing Station and Yudu Mountain gradually decreased, and the mountain-valley wind decreased by 8% and 6%, respectively. The weakening of local circulation may be related to the bidirectional feedback mechanism of the boundary layer and high concentrations of aerosols.

[Application of ARIMA Model for Mid- and Long-term Forecasting of Ozone Concentration].

Ozone pollution has recently become a severe air quality issue in the Beijing-Tianjin-Hebei region. Due to the lack of a precursor emission inventory and complexity of physical and chemical mechanism of ozone generation, numerical modeling still exhibits significant deviations in ozone forecasting. Owing to its simplicity and low calculation costs, the time series analysis model can be effectively applied for ozone pollution forecasting. We conducted a time series analysis of ozone concentration at Shangdianzi, Baoding, and Tianjin sites. Both seasonal and dynamic ARIMA models were established to perform mid- and long-term ozone forecasting. The correlation coefficient R between the predicted and observed value can reach 0.951, and the RMSE is only 10.2 µg·m-3 for the monthly average ozone prediction by the seasonal ARIMA model. The correlation coefficient R between the predicted and observed value increased from 0.296-0.455 to 0.670-0.748, and RMSE was effectively reduced for the 8-hour ozone average predicted by the dynamic ARIMA model.

[Characteristics of VOCs and Their Roles in Ozone Formation at a Regional Background Site in Beijing, China].

As important precursors of near-surface ozone, secondary organic aerosols (SOAs), and volatile organic compounds (VOCs) play an important role in photochemical reactions and fine particle formation. In this study, real-time VOCs were measured continuously by Syntech Spectras GC955 analyzers at the regional background site of the North China Plain from September 1 to 27, 2017. The VOC concentration levels, compositions, spatiotemporal variations, and the ozone formation potential during the observation period were investigated. The potential sources of initial VOCs indicated from the diagnostic ratios were further studied. The averaged total mixing ratio of VOCs was 12.53×10-9. Among all measured VOC species, alkanes were the most abundant species, which accounted for 65.3% of the total concentrations, followed by alkenes (26.7%) and aromatics (6.5%). In addition, the total OH radical loss rate of VOCs (L·OH) was 5.2 s-1. In particular, the contribution of C4-C5 alkenes to L·OH was as high as 61%, followed by C2-C3 alkenes, with a 12.8% contribution of L·OH. The average ozone formation potential of VOCs was 36.5×10-9. Among all the measured VOC species, alkenes were the most abundant species, which accounted for 71.2%. Among alkenes, the contribution of C4-C5 alkenes was the most prominent. Although the concentration of alkanes in Shangdianzi was much higher than other VOC species, the ozone formation potential was lower. Diagnostic ratios and source implications suggested that Shangdianzi was affected mainly by biomass/biofuel/coal burning, with substantially elevated benzene levels during the observation period, whereas a slight influence of traffic-related emissions was observed.

[Using Multiple Linear Regression Method to Evaluate the Impact of Meteorological Conditions and Control Measures on Air Quality in Beijing During APEC 2014].

Meteorological conditions have important impact on the diffusion and transport of air pollutants, thus separating and quantifying the impact of meteorological factors is a prerequisite for evaluation of air pollution control measures. Using observation data on SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 as well as meteorological factors at the Chaoyang site, an urban site in Beijing, we evaluated the impact of meteorological conditions and control measures on air quality in Beijing during APEC 2014 (from 15 October to 30 November, 2014) by the multiple linear regression method. The simulation performance of a multivariate linear regression model based on the parameters of meteorological factors for predicting pollutant concentration assuming constant emission conditions were ideal, produced a range of determination coefficient (R2) of 0.494-0.783. Our results suggested that air pollution control measures reduced the concentration of SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 by 48.3%, 53.5%, 18.7%, 40.6%, 3.6%, 34.8%, 28.8%, and 40.6%, while meteorological conditions reduced the concentration of SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 by 1.7%, -2.8%, 18.7%, 4.5%, 18.6%, 27.5%, 30.6%, and 35.6%. The combination of meteorological factors and control measures has significantly improved the air quality in Beijing during the APEC period. Control measures played a leading role in the reduction of SO2 and nitrogen oxides, and meteorological factors played a leading role in the reduction of CO. Meteorological factors and control measures made roughly equal contributions to the reduction of particulate matter. We also used the relative weight method to study the contribution of meteorological factors to the pollutant concentration. The results showed that the decisive meteorological factors on the concentrations of different pollutants were different.