[Variation in Pollutant Concentrations and Correlation Analysis with the Vegetation Index in Beijing-Tianjin-Hebei].

Based on 2017 hourly air quality monitoring data, NDVI 16 d synthetic data, and socio-economic data, the air pollution characteristics of Beijing-Tianjin-Hebei were systematically analyzed, and its variation, normalized vegetation index, and the relationship between the index (NDVI) and its impact on socio-economic factors, were analyzed by linear regression analysis and a geographically weighted regression model. The conclusions are as follows:①The overall air pollution in the Beijing-Tianjin-Hebei region is characterized by high-level pollution over the southern plain areas and low-level pollution over the northern mountainous areas. The air pollution increases from north to south, and shows significant spatial heterogeneity. ②From the perspective of seasonal changes, the overall order winter > autumn > spring > summer is observed, and atmospheric pollution in the Beijing-Tianjin-Hebei region shows significant temporal heterogeneity. ③The concentrations of pollutants such as SO2, NO2, CO, PM2.5, and PM10 all have a negative correlation with the NDVI value. Assuming that natural conditions such as climate and topography are relatively consistent, the lower the NDVI value, the more obvious the interference of human activities, the more concentrated the industrial economy layout, and the greater the pollution emissions, the more significant the negative impact on air quality. ④The NDVI reflects the land use, population distribution, and industrial layout to a certain extent, and these factors directly or indirectly determine the level of air pollution emissions and thus indicate the pollution distribution characteristics of the region. ⑤The results of the GWR model calculation show that the higher the level of economic development, the better the correlation between the NDVI and socioeconomic factors, PM2.5, and other pollutant concentrations. The distribution of the NDVI can generally reflect the level of social and economic development. The distribution of the NDVI also correlates to the distribution of PM2.5 to a certain extent.

Analysis of the Characteristics and Sources of Carbonaceous Aerosols in PM2.5 in the Beijing, Tianjin, and Langfang Region, China.

PM2.5 samples from Beijing, Tianjin, and Langfang were simultaneously collected from 20 November 2016 to 25 December 2016, and the organic carbon (OC) and elemental carbon (EC) content in the samples were measured and analyzed. The pollution characteristics and sources of OC and EC in atmospheric PM2.5 for three adjacent cities were discussed. The average mass concentrations of OC in PM2.5 in Beijing, Tianjin, and Langfang were 27.93 ± 23.35 µg/m³, 25.27 ± 12.43 µg/m³, and 52.75 ± 37.97 µg/m³, respectively, and the mean mass concentrations of EC were 6.61 ± 5.13 µg/m³, 6.14 ± 2.84 µg/m³, and 12.06 ± 6.81 µg/m³, respectively. The average mass concentration of total carbon (TC) accounted for 30.5%, 24.8%, and 49% of the average mass concentration of PM2.5 in the atmosphere. The total carbonaceous matter (TCA) in Beijing, Tianjin, and Langfang was 51.29, 46.57, and 96.45 µg/m³, respectively. The TCA was the main component of PM2.5 in the region. The correlation between OC and EC in the three cities showed R² values of 0.882, 0.633, and 0.784 for Beijing, Tianjin, and Langfang, respectively, indicating that the sources of urban carbonaceous aerosols had good consistency and stability. The OC/EC values of the three sampling points were 4.48 ± 1.45, 4.42 ± 1.77, and 4.22 ± 1.29, respectively, considerably greater than 2, indicating that the main sources of pollution were automobile exhaust, and the combustion of coal and biomass. The OC/EC minimum ratio method was used to estimate the secondary organic carbon (SOC) content in Beijing, Tianjin and Langfang. Their values were 10.73, 10.71, and 19.51, respectively, which accounted for 38%, 42%, and 37% of the average OC concentration in each city, respectively. The analysis of the eight carbon components showed that the main sources of pollutants in Beijing, Tianjin, and Langfang were exhaust emissions from gasoline vehicles, but the combustion of coal and biomass was relatively low. The pollution of road dust was more serious in Tianjin than in Beijing and Langfang. The contribution of biomass burning and coal-burning pollution sources to atmospheric carbon aerosols in Langfang was more prominent than that of Beijing and Tianjin.

Characteristics of PM2.5 Chemical Compositions and Their Effect on Atmospheric Visibility in Urban Beijing, China during the Heating Season.

Beijing, which is the capital of China, suffers from severe Fine Particles (PM2.5) pollution during the heating season. In order to take measures to control the PM2.5 pollution and improve the atmospheric environmental quality, daily PM2.5 samples were collected at an urban site from 15 November to 31 December 2016, characteristics of PM2.5 chemical compositions and their effect on atmospheric visibility were analyzed. It was found that the daily average mass concentrations of PM2.5 ranged from 7.64 to 383.00 µg m-3, with an average concentration of 114.17 µg m-3. On average, the Organic Carbon (OC) and Elemental Carbon (EC) contributed 21.39% and 5.21% to PM2.5, respectively. Secondary inorganic ions (SNA: SO4²- + NO3- + NH4⁺) dominated the Water-Soluble Inorganic Ions (WSIIs) and they accounted for 47.09% of PM2.5. The mass concentrations of NH4⁺, NO3- and SO42- during the highly polluted period were 8.08, 8.88 and 6.85 times greater, respectively, than during the clean period, which contributed most to the serious PM2.5 pollution through the secondary transformation of NO2, SO2 and NH3. During the highly polluted period, NH4NO3 contributed most to the reconstruction extinction coefficient (b'ext), accounting for 35.7%, followed by (NH4)2SO4 (34.44%) and Organic Matter (OM: 15.24%). The acidity of PM2.5 in Beijing was weakly acid. Acidity of PM2.5 and relatively high humidity could aggravate PM2.5 pollution and visibility impairment by promoting the generation of secondary aerosol. Local motor vehicles contributed the most to NO3-, OC, and visibility impairment in urban Beijing. Other sources of pollution in the area surrounding urban Beijing, including coal burning, agricultural sources, and industrial sources in the Hebei, Shandong, and Henan provinces, released large amounts of SO2, NH3, and NO2. These, which were transformed into SO42-, NH4⁺, and NO3- during the transmission process, respectively, and had a great impact on atmospheric visibility impairment.

Pollution Level and Health Risk Assessment of PM2.5-Bound Metals in Baoding City Before and After the Heating Period.

In order to assess the pollution levels and health risks of PM2.5-bound metals in Baoding City before and after the heating period, samples were collected in 2016 at Hebei University from September 25th to November 14th during the non-heating period, and November 15th to December 26th during the heating period, respectively. ICP-MS was applied to analyze seven heavy metals (Cr, Zn, Cu, Pb, Ni, Cd and Fe). The statistical analysis, enrichment factor (EF), pollution load index method, and Risk Assessment Method proposed by U.S. EPA were used to evaluate the non-carcinogenic risks of six of these heavy metals (Cr, Zn, Cu, Pb, Ni and Cd) and carcinogenic risks of three of these heavy metals (Cr, Ni and Cd). The results showed three main results. First, the average daily PM2.5 concentrations of the national air monitoring stations was 155.66 µg·m-3 which was 2.08 times as high as that of the second level criterion in China (75 µg·m-3) during the observation period. Compared with the non-heating period, all heavy metals concentrations increased during heating period. The growth rates of Pb and Ni were the highest and the lowest, which were 88.03 and 5.11 percent, respectively. Second, the results of enrichment factor indicated that the EF values of all heavy metals were higher during the heating period in comparison with during the non-heating period, but the degree of enrichment of all heavy metals remained unchanged. Not only those, Cr and Ni were minimally enriched and were affected by both human and natural factors, Pb, Cu and Zn were significantly enriched and were mainly affected by human factors, the enrichment of Cd was much higher than that of the other heavy metals, exhibiting extremely high enrichment, mainly due to human factors during the whole sampling period. The results of the pollution load index indicated that the proportions of the number of highly and very highly polluted PM2.5-bound metals were the highest during the heating period, while the proportion of moderately polluted PM2.5-bound metals was the highest during the non-heating period. The combined pollution degree of heavy metals was more serious during the heating period. Third, according to the health risk assessment model, we concluded that the non-carcinogenic and carcinogenic risks caused by inhalation exposure were the highest and by dermal exposure were the lowest for all kinds of people. The overall non-carcinogenic risk of heavy metals via inhalation and subsequent ingestion exposure caused significant harm to children during the non-heating and the heating periods, and the risk values were 2.64, 4.47, 1.20 and 1.47, respectively. Pb and Cr exhibited the biggest contributions to the non-carcinogenic risk. All the above non-carcinogenic risks exceeded the standard limits suggested by EPA (HI or HQ < 1). The carcinogenic risk via inhalation exposure to children, adult men and women were 2.10 × 10-4, 1.80 × 10-4, and 1.03 × 10-4 during the non-heating period, respectively, and 2.52 × 10-4, 2.16 × 10-4 and 1.23 × 10-4 during the heating period, respectively. All the above carcinogenic risks exceeded the threshold ranges (10-6~10-4), and Cr posed a carcinogenic risk to all people.