[Discussion of Air Quality Forecasting Evaluation for Cities Based on Half-level Method].

Ambient air quality forecasting evaluation plays an important role in improving forecasting capability. In order to provide better support for refined air quality management, with reference to the UK air quality forecasting evaluation method, this study divided six air quality index (AQI) levels into 12 half-levels and explored AQI, PM2.5, and O3-8h concentration forecasting evaluation based on the half-level method in "2+26" cities during 2020. Comparison with the AQI range forecasting and AQI level range forecasting evaluation showed that the half-level forecasting evaluation method could combine these two indicators into one, providing feasibility and application value in operational air quality forecasting evaluation. Specific half-level forecasting evaluation showed that the forecasting effect of AQI and O3-8h concentration at the low and high levels was significantly worse than that of the middle levels in "2+26" cities. The forecasting effect of the PM2.5 concentration was relatively stable in different half-levels. The monthly variation curves of AQI, PM2.5, and O3-8h concentration forecasting accuracy exhibited a bimodal pattern, first rising and then falling and a flat pattern, respectively. The overestimate of PM2.5 concentration forecasting was significant in each month. The accuracy gaps of AQI and O3-8h concentration forecasting in different cities was relatively small; however, the PM2.5 concentration forecasting accuracy fluctuated greatly. The AQI forecasting accuracies of Beijing and Tianjin were higher than that of neighboring provinces. Additionally, the PM2.5 and O3-8h concentration forecasting effects in Beijing and Henan province were relatively the best.

[Comparison and Analysis of PM2.5 Forecast in Key Areas Based on the Neural Network Model and Numerical Model].

The PM2.5 forecast models of 95 cities in Beijing-Tianjin-Hebei and its surrounding cities (BTH); the Fenwei Plain (FWP); the border area of Jiangsu, Anhui, Shandong, and Henan (JASH); and the Yangtze River Delta (YRD) regions were established using BP neural network models, and the forecast was carried out for the next seven days in the autumn and winter in 2020. By comparing the forecast results of the BP neural network models, numerical model, and artificial correction, the PM2.5 forecast effects of the three methods were analyzed and evaluated. The results showed:① The performance of the short-term forecast based on the BP neural network was relatively good but was reduced in the medium and long term and systematically overestimated in four regions. The numerical model effects were lower than those of the BP neural network models. ② The accuracy rates of the PM2.5 forecast concentration by the three methods were generally low in the four regions, with an average of less than 50%, and the accuracy values in order from high to low were the BP neural network models, artificial correction, and the numerical model. The accuracy rates of IAQI levels of PM2.5 were significantly improved by the three methods, and the averages were above 65% in the first four days. The effects of the BP neural network models and artificial correction were similar, which were generally higher than those of the numerical model. ③ The numerical model had good effects in the BTH, JASH, and YRD regions, whereas it was the worst when forecasting moderately and above-polluted days in the FWP region. The BP neural network model had a good performance when forecasting short-term PM2.5 in the BTH, JASH, and FWP regions, whereas it was poor in the YRD region. In general, the performance of artificial correction was relatively good when forecasting moderate-level days and was close to the BP neural network model when forecasting heavily polluted days.

[Assessment of Emergency Emission Reduction Effect During the Heavy Air Pollution Episodes in Beijing, Tianjin, Hebei, and Its Surrounding Area("2+26" Cities) from October to December 2019].

To evaluate the effect of emergency emission reduction measures during the heavy air pollution episodes in Beijing, Tianjin, Hebei, and its surrounding areas, a scenario simulation method was used. The concentrations of PM2.5, PM10, SO2, NO2, CO, and O3-8h, air quality index (AQI), characteristics of heavy air pollution, and climate and meteorological factors were analyzed using the observation data available from October to December 2019. The 24 h, 72 h, and 144 h prediction results of NAQPMS model were analyzed. The uncertainties of the assessment and model prediction were discussed. The results showed that the average PM2.5 concentration in Beijing, Tianjin, and its surrounding 26 cities ("2+26" cities) from October to December 2019 was 64 µg ·m-3, indicating a decrease of 10 µg ·m-3 as compared with that during the same period in 2018. There were 4 occurrences of regional heavy air pollution episodes, with the average PM2.5 concentration of 156 µg ·m-3 of affected cities. The value of evaluation on meteorological condition index of PM2.5 pollution (EMI) of "2+26" cities ranged from -15.6%-16.8%. The meteorological conditions of 12 cities, including Beijing, Tianjin, and Shijiazhuang, deteriorated as compared with those during the same period in 2018, and the changes ranged from 3.2%-16.8%. However, the emergency emission reduction measures effectively reduced the occurrence of regional heavy air pollution episodes, the peak concentration of PM2.5 was decreased significantly, and no severe regional pollution episode occurred. The daily PM2.5 concentrations reduced by 2% to 9% in Beijing, Shijiazhuang, Baoding, Tangshan, and other cities during a typical heavy air pollution period. The quarterly average concentrations of PM2.5 in the "2+26" cities reduced by 1 to 3 µg ·m-3. The regional emergency emission reduction measures have played an active role in protecting the health of the people and improving the quality of ambient air.

[Heavy Pollution Characteristics and Assessment of PM2.5 Predicted Model Results in Beijing-Tianjin-Hebei Region and Surrounding Areas During November 23 to December 4, 2018].

This study discusses the concentration characteristics of PM2.5 and PM10, as well as pollution meteorology in large-scale and long-term heavy pollution in the Beijing-Tianjin-Hebei region and its surrounding areas from November 23 to December 4, 2018, where the primary pollutants are comprised of PM2.5 and PM10. The monitoring results obtained from ground-based and vehicle-mounted lidars, as well as the HYSPLIT-4 backward trajectory combined with meteorological factors analysis are discussed. The accuracy and uncertainty of the air quality forecast model of NAQPMS, CMAQ, and CAMx during heavy air pollution were analyzed retrospectively. The results show that PM2.5 and sand dust in most cities in the south-central region contribute to severe pollution levels. The hourly peak concentrations of PM10 in Zhangjiakou, Beijing, Shijiazhuang, Handan, and Zhengzhou were 1589, 864, 794, 738, and 766 µg·m-3, respectively. The respective hourly peak concentrations of PM2.5 were 239, 319, 387, 321, and 380 µg·m-3. Ground static pressure field, high humidity, inversion, and other static and stable conditions, as well as sand dust transmitted from the northwest, were important pollution meteorological and weather factors. The monitoring data of ground-based lidar and vehicle-mounted lidar combined with the HYSPLIT-4 backward trajectory analysis showed that the air pollutant transmitted from the Southwest and Southeast during the heavy pollution period was primarily PM2.5. The air pollutant transmitted from the Northwest during the two sand dust processes. Moreover, the model of NAQPMS, CMAQ, and CAMx performed well in forecasting the heavy pollution process in the Beijing-Tianjin-Hebei region and its surrounding areas. However there are slight deviations for some individual cities, related to uncertainty in the meteorological model prediction, atmospheric chemical reaction mechanism, and pollution source list. Furthermore, the reduction in pollution source emissions caused by pollution emergency measures was also one of the main reasons for the overestimation.

[Concentration Characteristics and Assessment of Model-Predicted Results of PM2.5 in the Beijing-Tianjin-Hebei Region in Autumn and Winter].

This paper discusses the concentration characteristics of PM2.5, as well as its relationship with meteorological factors in autumn and winter (from September to the following February), from 2013 to 2018 in the Beijing-Tianjin-Hebei (BTH) region. The accuracy and uncertainty of the air quality forecast models NAQPMS(nested air quality prediction modeling system), CMAQ(community multiscale air quality modeling system), and CAMx (comprehensive air quality model with extensions) were analyzed based on the model-predicted and measured PM2.5 concentration in autumn and winter from 2015 to 2018. The accuracy of NAQPMS, CMAQ, and CAMx during typical heavy air pollution was also tested. Moreover, methods to improve the accuracy of the model forecast were discussed. The results showed that the mean concentrations of PM2.5 in the BTH region were 122, 98, 82, 99, and 65 µg·m-3 in the five autumn and winter periods, respectively. When the air quality index (AQI) exceeded 150 during each autumn and winter, it reached 229, 198, 210, 204, and 180 µg·m-3, respectively. There were 64 occurrences of heavy regional PM2.5 air pollution in autumn and winter from 2013 to 2018. The average duration was longest in the 2013 to 2014 period, and shortest in the 2017 to 2018 period. The peak concentration and average concentration of PM2.5 decreased year on year, except for the period from 2016 to 2017. In autumn and winter, PM2.5 concentration had a relatively close relationship with relative humidity, wind and sunshine duration, compared with a weak relationship with temperature and air pressure. Regional heavy air pollution always happened under the condition of low wind speed(less than 2 m·s-1),higher relative humidity(greater than 65%),and southwest and northeast wind direction. In addition, the heavy air pollution of PM2.5 in BTH in autumn and winter can be effectively forecasted by NAQPMS, CMAQ, and CAMx. The predicted and measured PM2.5 concentration showed a close relationship. The models performed well in forecasting Zhangjiakou, Chengde, and Qinhuangdao, but by contrast overestimated in Tangshan, Shijiazhuang, Baoding, Beijing, and Tianjin. The uncertainty of emission sources, measured and predicted meteorological data, and the atmospheric chemical reaction mechanism may be the main reasons for the overestimate.

[Assessment of Heavy Metals Pollution and Its Health Risk of Atmospheric Dust Fall from East Part of Junggar Basin in Xinjiang].

In order to evaluate the contamination and health risk of heavy metals from atmospheric dust fall in Zhundong opencast coalfield in Xinjiang, samples of atmospheric dust fall were collected from 52 sampling sites covering the entire region and the contents of Zn, Cu, Cr, Pb, Hg and As were tested and analyzed. The contamination was assessed by geo-accumulation index method, and the risk to human health was assessed using the US EPA Health Risk Assessment Model. The results showed that:The contamination of heavy metals from atmospheric dust fall had a significant difference, in the order of Zn > Cr > Cu > As > Pb > Hg, and the average contents were higher than the soil background of Changji. The coefficient of variation of Hg, Cu and As was 381.91%, 99.94% and 97.82%, and human activities had a greater impact on them. The correlation coefficients in 6 heavy metals were complex, the correlation coefficients among Zn-Cu-Cr were more relevant than Hg-As-Pb. The assessment results of geo-accumulation index indicated that the Zn pollution in the atmospheric dust fall should be classified as extreme degree, and that of Cu, Pb, As as between slight and extreme degrees, and Hg as practically uncontaminated. The exposure content of carcinogenic risk and non-carcinogenic risk of the study area had little difference. It was HQCr > HQAs > HQZn > HQPb > HQCu > HQHg, the total non-cancer hazard index was 0.258, the non-cancer hazard indexes were both lower than their threshold values, suggesting that they would not harm the health. The carcinogenic risk hazard indexes were in the order of CRAs > CRCr > CRPb, suggesting that Pb had no cancer risk, while As was the most important carcinogenic factor. The average TCR was 1.95E-05, indicating that the risk was within the limit that human can tolerate.

[Spatial Distribution Characteristics of Heavy Metal Pollution and Health Risk in Soil Around the Coal Industrial Area of East Junggar Basin].

The soil around the coal industrial area of East Junggar Basin in Xinjiang was studied. A total of 64 soil samples were collected from the 0-10 cm, 10-20 cm, 20-30 cm layers of soil profile, and the contents of Zn, Cu, Cr, Pb, Hg and As were tested, respectively. Pollution Load Index(PLI) was employed to assess the heavy metal contents and the model of health risk assessment recommended by USEPA was adopted to evaluate the health risk due to exposure to heavy metals in different soil depths. The multivariate statistical analysis, geostatistical analysis and GIS technology then were used to study the differences, spatial variability structure and distribution pattern of the evaluated results, and cross-validation method was adopted to assess the prediction results and its stability. The results suggested that the ranges of Zn, Cu, Pb contents were 46.06-48.00 mg·kg-1, 18.37-19.271 mg·kg-1 and 11.30-13.29 mg·kg-1, which did not exceed the standard compared with the background values of soil in Xinjiang. The ranges of Cr, Hg, As contents were 80.29-85.42 mg·kg-1,0.06-0.07 mg·kg-1,30.64-31.52 mg·kg-1, all of which exceeded the standard compared with the background values of soil in Xinjiang, and the exceeded rate was 60%. The values of PLI were in the order of PLI0-10 cm(1.35) > PLI20-30 cm(1.28) > PLI10-20 cm(1.25), which belonged to slightly polluted level. The values of HI were in the order of HI0-10 cm(2.53E-01) > HI20-30 cm(2.48E-01) > HI10-20 cm(2.43E-01), which indicated there was no non-carcinogenic risk. The values of TCR were in the order of TCR0-10 cm(2.81E-05) > TCR20-30 cm(2.80E-05) > TCR10-20 cm(2.74E-05), which was the acceptable level of carcinogenic risk. According to One -way ANOVA analyses, there was no noticeable difference in the PLI, HI, TCR (α is 0.863, 0.134, 0.056 respectively). Geo-statistical Analysis results implied that the regions with high contents of Zn, Cu and As were distributed near the coal industrial area and Northern part of study area in the 0-10 cm soil layer, Pb formed V-shaped high content ribbon, high content of Hg was located in the middle and Southern area, and high content of Cr was located in Coal Industrial Area and the anterior radial decline. High values of PLI, HI and TCR were found in north of the study area. The moderate pollution region of PLI decreased with the increase of soil depth, whereas HI and TCR showed no significant change. On the whole, high degree of heavy metals pollution and high possibility of health risk were mainly distributed around the six coal industrial areas which are in the high density population zone. Especially, the pollution of Cr, Hg, As was relatively serious and the health risk of As was the most serious which should be attached great importance to.